diff --git a/M2/Reinforcement Learning/project/Project_RL_DANJOU_VON-SIEMENS.ipynb b/M2/Reinforcement Learning/project/Project_RL_DANJOU_VON-SIEMENS.ipynb
index 11332d9..81734cd 100644
--- a/M2/Reinforcement Learning/project/Project_RL_DANJOU_VON-SIEMENS.ipynb	
+++ b/M2/Reinforcement Learning/project/Project_RL_DANJOU_VON-SIEMENS.ipynb	
@@ -17,27 +17,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 51,
    "id": "b50d7174",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using device: mps\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pickle\n",
     "from collections import deque\n",
     "from pathlib import Path\n",
     "\n",
     "import ale_py  # noqa: F401 — registers ALE environments\n",
-    "import gymnasium as gym\n",
-    "import supersuit as ss\n",
-    "from gymnasium.wrappers import FrameStackObservation, ResizeObservation\n",
     "from pettingzoo.atari import tennis_v3\n",
     "from tqdm.auto import tqdm\n",
     "\n",
@@ -45,10 +34,7 @@
     "import numpy as np\n",
     "\n",
     "import torch\n",
-    "from torch import nn, optim\n",
-    "\n",
-    "DEVICE = torch.device(\"mps\" if torch.backends.mps.is_available() else \"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "print(f\"Using device: {DEVICE}\")\n"
+    "from torch import nn, optim\n"
    ]
   },
   {
@@ -66,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 52,
    "id": "ff3486a4",
    "metadata": {},
    "outputs": [],
@@ -102,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 53,
    "id": "be85c130",
    "metadata": {},
    "outputs": [],
@@ -110,9 +96,6 @@
     "def normalize_obs(observation: np.ndarray) -> np.ndarray:\n",
     "    \"\"\"Flatten and normalize an observation to a 1D float64 vector.\n",
     "\n",
-    "    Replicates the /255.0 normalization used in all agents from the original project.\n",
-    "    For image observations of shape (4, 84, 84), this produces a vector of length 28_224.\n",
-    "\n",
     "    Args:\n",
     "        observation: Raw observation array from the environment.\n",
     "\n",
@@ -155,7 +138,6 @@
     "    if rng.random() < epsilon:\n",
     "        return int(rng.integers(0, q_values.size))\n",
     "\n",
-    "    # Handle NaN/Inf values safely\n",
     "    finite_mask = np.isfinite(q_values)\n",
     "    if not np.any(finite_mask):\n",
     "        return int(rng.integers(0, q_values.size))\n",
@@ -186,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 54,
    "id": "ded9b1fb",
    "metadata": {},
    "outputs": [],
@@ -214,8 +196,9 @@
     "        next_state: np.ndarray,\n",
     "        done: bool,\n",
     "        next_action: int | None = None,\n",
+    "        agent_id: str = \"single\",\n",
     "    ) -> None:\n",
-    "        \"\"\"Update agent parameters from one transition.\"\"\"\n",
+    "        \"\"\"Update the agent's internal state based on a transition.\"\"\"\n",
     "\n",
     "    def save(self, filename: str) -> None:\n",
     "        \"\"\"Save the agent state to disk using pickle.\"\"\"\n",
@@ -225,7 +208,21 @@
     "    def load(self, filename: str) -> None:\n",
     "        \"\"\"Load the agent state from disk.\"\"\"\n",
     "        with Path(filename).open(\"rb\") as f:\n",
-    "            self.__dict__.update(pickle.load(f))  # noqa: S301\n"
+    "            path = Path(filename).resolve()\n",
+    "            ckpt_root = CHECKPOINT_DIR.resolve()\n",
+    "            if ckpt_root not in path.parents:\n",
+    "                msg = f\"Refusing to load checkpoint outside '{ckpt_root}': {path}\"\n",
+    "                raise ValueError(msg)\n",
+    "            if path.suffix != \".pkl\":\n",
+    "                msg = f\"Unsupported checkpoint format: {path.suffix}\"\n",
+    "                raise ValueError(msg)\n",
+    "\n",
+    "            state = pickle.load(f)  # noqa: S301 - trusted local checkpoints only\n",
+    "            if not isinstance(state, dict):\n",
+    "                msg = \"Invalid checkpoint payload: expected a dict.\"\n",
+    "                raise TypeError(msg)\n",
+    "\n",
+    "            self.__dict__.update(state)\n"
    ]
   },
   {
@@ -240,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 55,
    "id": "78bdc9d2",
    "metadata": {},
    "outputs": [],
@@ -273,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 56,
    "id": "c124ed9a",
    "metadata": {},
    "outputs": [],
@@ -312,8 +309,6 @@
     "        self.n_features = n_features\n",
     "        self.alpha = alpha\n",
     "        self.gamma = gamma\n",
-    "        # Weight matrix: one row per action, analogous to Lab 7's self.w\n",
-    "        # but organized as (n_actions, n_features) for dense features.\n",
     "        self.W = np.zeros((n_actions, n_features), dtype=np.float64)\n",
     "\n",
     "    def _q_values(self, phi: np.ndarray) -> np.ndarray:\n",
@@ -329,7 +324,7 @@
     "            Array of Q-values, shape (n_actions,).\n",
     "\n",
     "        \"\"\"\n",
-    "        return self.W @ phi  # shape (n_actions,)\n",
+    "        return self.W @ phi\n",
     "\n",
     "    def get_action(self, observation: np.ndarray, epsilon: float = 0.0) -> int:\n",
     "        \"\"\"Select action using ε-greedy policy over linear Q-values.\n",
@@ -349,6 +344,7 @@
     "        next_state: np.ndarray,\n",
     "        done: bool,\n",
     "        next_action: int | None = None,\n",
+    "        agent_id: str = \"single\",\n",
     "    ) -> None:\n",
     "        \"\"\"Perform one semi-gradient SARSA update.\n",
     "\n",
@@ -359,37 +355,25 @@
     "        In continuous form with linear approximation (Lab 7 SarsaAgent.update):\n",
     "            delta = target - q(s, a)\n",
     "            W[a] += alpha * delta * phi(s)\n",
-    "\n",
-    "        Args:\n",
-    "            state: Current observation.\n",
-    "            action: Action taken.\n",
-    "            reward: Reward received.\n",
-    "            next_state: Next observation.\n",
-    "            done: Whether the episode ended.\n",
-    "            next_action: Action chosen in next state (required for SARSA).\n",
-    "\n",
     "        \"\"\"\n",
     "        phi = np.nan_to_num(normalize_obs(state), nan=0.0, posinf=0.0, neginf=0.0)\n",
-    "        q_sa = float(self.W[action] @ phi)  # current estimate q(s, a)\n",
+    "        q_sa = float(self.W[action] @ phi)\n",
     "        if not np.isfinite(q_sa):\n",
     "            q_sa = 0.0\n",
     "\n",
     "        if done:\n",
-    "            # Terminal: no future value (Lab 5B: gamma * Q[s2, a2] * 0)\n",
     "            target = reward\n",
     "        else:\n",
-    "            # On-policy: use q(s', a') where a' is the actual next action\n",
-    "            # This is the key SARSA property (Lab 5B)\n",
-    "            phi_next = np.nan_to_num(normalize_obs(next_state), nan=0.0, posinf=0.0, neginf=0.0)\n",
+    "            phi_next = np.nan_to_num(\n",
+    "                normalize_obs(next_state), nan=0.0, posinf=0.0, neginf=0.0,\n",
+    "            )\n",
     "            if next_action is None:\n",
-    "                next_action = 0  # fallback, should not happen in practice\n",
+    "                next_action = 0\n",
     "            q_sp_ap = float(self.W[next_action] @ phi_next)\n",
     "            if not np.isfinite(q_sp_ap):\n",
     "                q_sp_ap = 0.0\n",
     "            target = float(reward) + self.gamma * q_sp_ap\n",
     "\n",
-    "        # Semi-gradient update: W[a] += alpha * delta * phi(s)\n",
-    "        # Analogous to Lab 7: self.w[idx] += self.alpha * delta\n",
     "        if not np.isfinite(target):\n",
     "            return\n",
     "\n",
@@ -418,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 57,
    "id": "f5b5b9ea",
    "metadata": {},
    "outputs": [],
@@ -476,6 +460,7 @@
     "        next_state: np.ndarray,\n",
     "        done: bool,\n",
     "        next_action: int | None = None,\n",
+    "        agent_id: str = \"single\",\n",
     "    ) -> None:\n",
     "        \"\"\"Perform one Q-learning update.\n",
     "\n",
@@ -487,21 +472,19 @@
     "            delta = target - q(s, a)\n",
     "            W[a] += alpha * delta * phi(s)\n",
     "        \"\"\"\n",
-    "        _ = next_action  # Q-learning is off-policy: next_action is not used\n",
+    "        _ = next_action\n",
     "        phi = np.nan_to_num(normalize_obs(state), nan=0.0, posinf=0.0, neginf=0.0)\n",
     "        q_sa = float(self.W[action] @ phi)\n",
     "        if not np.isfinite(q_sa):\n",
     "            q_sa = 0.0\n",
     "\n",
     "        if done:\n",
-    "            # Terminal state: no future value\n",
-    "            # Lab 5B: gamma * np.max(Q[s2]) * (0 if terminated else 1)\n",
     "            target = reward\n",
     "        else:\n",
-    "            # Off-policy: use max over all actions in next state\n",
-    "            # This is the key Q-learning property (Lab 5B)\n",
-    "            phi_next = np.nan_to_num(normalize_obs(next_state), nan=0.0, posinf=0.0, neginf=0.0)\n",
-    "            q_next_all = self._q_values(phi_next)  # q(s', a') for all a'\n",
+    "            phi_next = np.nan_to_num(\n",
+    "                normalize_obs(next_state), nan=0.0, posinf=0.0, neginf=0.0,\n",
+    "            )\n",
+    "            q_next_all = self._q_values(phi_next)\n",
     "            q_next_max = float(np.max(q_next_all))\n",
     "            if not np.isfinite(q_next_max):\n",
     "                q_next_max = 0.0\n",
@@ -540,28 +523,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 58,
    "id": "7a3aa454",
    "metadata": {},
    "outputs": [],
    "source": [
     "class MonteCarloAgent(Agent):\n",
-    "    \"\"\"Monte Carlo control agent with linear function approximation.\n",
-    "\n",
-    "    Inspired by Lab 4 mc_control_epsilon_soft:\n",
-    "    - Accumulates transitions in an episode buffer\n",
-    "    - At episode end (done=True), computes discounted returns backward:\n",
-    "        G = gamma * G + r  (same as Lab 4's reversed loop)\n",
-    "    - Updates weights with semi-gradient: W[a] += alpha * (G - q(s,a)) * phi(s)\n",
-    "\n",
-    "    Unlike TD methods (SARSA, Q-Learning), no update occurs until the episode ends.\n",
-    "\n",
-    "    Performance optimizations over naive per-step implementation:\n",
-    "    - float32 weights & features (halves memory bandwidth, faster SIMD)\n",
-    "    - Raw observations stored compactly as uint8, batch-normalized at episode end\n",
-    "    - Vectorized return computation & chunk-based weight updates via einsum\n",
-    "    - Single weight sanitization per episode instead of per-step\n",
-    "    \"\"\"\n",
+    "    \"\"\"Monte Carlo control agent with linear function approximation.\"\"\"\n",
     "\n",
     "    def __init__(\n",
     "        self,\n",
@@ -577,15 +545,16 @@
     "        self.alpha = alpha\n",
     "        self.gamma = gamma\n",
     "        self.W = np.zeros((n_actions, n_features), dtype=np.float32)\n",
-    "        self._obs_buf: list[np.ndarray] = []\n",
-    "        self._act_buf: list[int] = []\n",
-    "        self._rew_buf: list[float] = []\n",
+    "        self._obs_buf: dict[str, list[np.ndarray]] = {}\n",
+    "        self._act_buf: dict[str, list[int]] = {}\n",
+    "        self._rew_buf: dict[str, list[float]] = {}\n",
     "\n",
     "    def _q_values(self, phi: np.ndarray) -> np.ndarray:\n",
+    "        \"\"\"Compute Q-values.\"\"\"\n",
     "        return self.W @ phi\n",
     "\n",
     "    def get_action(self, observation: np.ndarray, epsilon: float = 0.0) -> int:\n",
-    "        \"\"\"Select action using ε-greedy policy over linear Q-values.\"\"\"\n",
+    "        \"\"\"Select action using epsilon-greedy policy over linear Q-values.\"\"\"\n",
     "        phi = observation.flatten().astype(np.float32) / np.float32(255.0)\n",
     "        q_vals = self._q_values(phi)\n",
     "        return epsilon_greedy(q_vals, epsilon, self.rng)\n",
@@ -598,25 +567,31 @@
     "        next_state: np.ndarray,\n",
     "        done: bool,\n",
     "        next_action: int | None = None,\n",
+    "        agent_id: str = \"single\",\n",
     "    ) -> None:\n",
     "        \"\"\"Accumulate episode transitions and update weights at episode end.\"\"\"\n",
     "        _ = next_state, next_action\n",
     "\n",
-    "        self._obs_buf.append(state)\n",
-    "        self._act_buf.append(action)\n",
-    "        self._rew_buf.append(reward)\n",
+    "        if agent_id not in self._obs_buf:\n",
+    "            self._obs_buf[agent_id] = []\n",
+    "            self._act_buf[agent_id] = []\n",
+    "            self._rew_buf[agent_id] = []\n",
+    "\n",
+    "        self._obs_buf[agent_id].append(state)\n",
+    "        self._act_buf[agent_id].append(action)\n",
+    "        self._rew_buf[agent_id].append(reward)\n",
     "\n",
     "        if not done:\n",
     "            return\n",
     "\n",
-    "        n = len(self._rew_buf)\n",
-    "        actions = np.array(self._act_buf, dtype=np.intp)\n",
+    "        n = len(self._rew_buf[agent_id])\n",
+    "        actions = np.array(self._act_buf[agent_id], dtype=np.intp)\n",
     "\n",
     "        returns = np.empty(n, dtype=np.float32)\n",
     "        G = np.float32(0.0)\n",
     "        gamma32 = np.float32(self.gamma)\n",
     "        for i in range(n - 1, -1, -1):\n",
-    "            G = gamma32 * G + np.float32(self._rew_buf[i])\n",
+    "            G = gamma32 * G + np.float32(self._rew_buf[agent_id][i])\n",
     "            returns[i] = G\n",
     "\n",
     "        alpha32 = np.float32(self.alpha)\n",
@@ -625,7 +600,7 @@
     "            end = min(start + chunk_size, n)\n",
     "            cs = end - start\n",
     "\n",
-    "            raw = np.array(self._obs_buf[start:end])\n",
+    "            raw = np.array(self._obs_buf[agent_id][start:end])\n",
     "            phi = raw.reshape(cs, -1).astype(np.float32)\n",
     "            phi /= np.float32(255.0)\n",
     "\n",
@@ -642,9 +617,24 @@
     "\n",
     "        self.W = np.nan_to_num(self.W, nan=0.0, posinf=1e6, neginf=-1e6)\n",
     "\n",
+    "        self._obs_buf[agent_id].clear()\n",
+    "        self._act_buf[agent_id].clear()\n",
+    "        self._rew_buf[agent_id].clear()\n",
+    "\n",
+    "    def save(self, filename: str) -> None:\n",
+    "        \"\"\"Save agent state, clearing episode buffers first to avoid stale data.\"\"\"\n",
     "        self._obs_buf.clear()\n",
     "        self._act_buf.clear()\n",
-    "        self._rew_buf.clear()\n"
+    "        self._rew_buf.clear()\n",
+    "        super().save(filename)\n",
+    "\n",
+    "    def load(self, filename: str) -> None:\n",
+    "        \"\"\"Load agent state and ensure correct dtype for weights.\"\"\"\n",
+    "        super().load(filename)\n",
+    "        self.W = np.asarray(self.W, dtype=np.float32)\n",
+    "        self._obs_buf = {}\n",
+    "        self._act_buf = {}\n",
+    "        self._rew_buf = {}\n"
    ]
   },
   {
@@ -670,7 +660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 59,
    "id": "9c777493",
    "metadata": {},
    "outputs": [],
@@ -738,8 +728,9 @@
     "        lr: float = 1e-4,\n",
     "        gamma: float = 0.99,\n",
     "        buffer_size: int = 50_000,\n",
-    "        batch_size: int = 128,\n",
+    "        batch_size: int = 64,\n",
     "        target_update_freq: int = 1000,\n",
+    "        train_freq: int = 4,\n",
     "        seed: int = 42,\n",
     "    ) -> None:\n",
     "        \"\"\"Initialize DQN agent.\n",
@@ -752,6 +743,7 @@
     "            buffer_size: Maximum replay buffer capacity.\n",
     "            batch_size: Minibatch size for updates.\n",
     "            target_update_freq: Steps between target network syncs.\n",
+    "            train_freq: Number of steps between each training update.\n",
     "            seed: RNG seed.\n",
     "\n",
     "        \"\"\"\n",
@@ -761,19 +753,20 @@
     "        self.gamma = gamma\n",
     "        self.batch_size = batch_size\n",
     "        self.target_update_freq = target_update_freq\n",
+    "        self.train_freq = train_freq\n",
     "        self.update_step = 0\n",
+    "        self.train_step = 0\n",
     "\n",
-    "        # Q-network and target network on GPU\n",
     "        torch.manual_seed(seed)\n",
-    "        self.q_net = QNetwork(n_features, n_actions).to(DEVICE)\n",
-    "        self.target_net = QNetwork(n_features, n_actions).to(DEVICE)\n",
+    "        self.device = torch.device(\"cpu\")\n",
+    "        self.q_net = QNetwork(n_features, n_actions).to(self.device)\n",
+    "        self.target_net = QNetwork(n_features, n_actions).to(self.device)\n",
     "        self.target_net.load_state_dict(self.q_net.state_dict())\n",
     "        self.target_net.eval()\n",
     "\n",
     "        self.optimizer = optim.Adam(self.q_net.parameters(), lr=lr)\n",
-    "        self.loss_fn = nn.SmoothL1Loss()  # Huber loss — more robust than MSE\n",
+    "        self.loss_fn = nn.SmoothL1Loss()\n",
     "\n",
-    "        # Experience replay buffer\n",
     "        self.replay_buffer = ReplayBuffer(buffer_size)\n",
     "\n",
     "    def get_action(self, observation: np.ndarray, epsilon: float = 0.0) -> int:\n",
@@ -783,8 +776,8 @@
     "\n",
     "        phi = normalize_obs(observation)\n",
     "        with torch.no_grad():\n",
-    "            state_t = torch.from_numpy(phi).float().unsqueeze(0).to(DEVICE)\n",
-    "            q_vals = self.q_net(state_t).cpu().numpy().squeeze(0)\n",
+    "            state_t = torch.from_numpy(phi).float().unsqueeze(0).to(self.device)\n",
+    "            q_vals = self.q_net(state_t).numpy().squeeze(0)\n",
     "        return epsilon_greedy(q_vals, 0.0, self.rng)\n",
     "\n",
     "    def update(\n",
@@ -795,49 +788,51 @@
     "        next_state: np.ndarray,\n",
     "        done: bool,\n",
     "        next_action: int | None = None,\n",
+    "        agent_id: str = \"single\",\n",
     "    ) -> None:\n",
     "        \"\"\"Store transition and perform a minibatch DQN update.\"\"\"\n",
-    "        _ = next_action  # DQN is off-policy\n",
+    "        _ = next_action\n",
     "\n",
-    "        # Store transition\n",
     "        phi_s = normalize_obs(state)\n",
     "        phi_sp = normalize_obs(next_state)\n",
     "        self.replay_buffer.push(phi_s, action, reward, phi_sp, done)\n",
     "\n",
-    "        if len(self.replay_buffer) < self.batch_size:\n",
+    "        self.update_step += 1\n",
+    "        # Optimize: only train every `train_freq` steps to save computation\n",
+    "        if (\n",
+    "            len(self.replay_buffer) < self.batch_size\n",
+    "            or self.update_step % self.train_freq != 0\n",
+    "        ):\n",
     "            return\n",
     "\n",
-    "        # Sample minibatch\n",
-    "        states_b, actions_b, rewards_b, next_states_b, dones_b = self.replay_buffer.sample(\n",
-    "            self.batch_size, self.rng,\n",
+    "        states_b, actions_b, rewards_b, next_states_b, dones_b = (\n",
+    "            self.replay_buffer.sample(\n",
+    "                self.batch_size,\n",
+    "                self.rng,\n",
+    "            )\n",
     "        )\n",
     "\n",
-    "        # Convert to tensors on device\n",
-    "        states_t = torch.from_numpy(states_b).float().to(DEVICE)\n",
-    "        actions_t = torch.from_numpy(actions_b).long().to(DEVICE)\n",
-    "        rewards_t = torch.from_numpy(rewards_b).float().to(DEVICE)\n",
-    "        next_states_t = torch.from_numpy(next_states_b).float().to(DEVICE)\n",
-    "        dones_t = torch.from_numpy(dones_b).float().to(DEVICE)\n",
+    "        states_t = torch.from_numpy(states_b).float().to(self.device)\n",
+    "        actions_t = torch.from_numpy(actions_b).long().to(self.device)\n",
+    "        rewards_t = torch.from_numpy(rewards_b).float().to(self.device)\n",
+    "        next_states_t = torch.from_numpy(next_states_b).float().to(self.device)\n",
+    "        dones_t = torch.from_numpy(dones_b).float().to(self.device)\n",
     "\n",
-    "        # Current Q-values for taken actions\n",
     "        q_values = self.q_net(states_t)\n",
     "        q_curr = q_values.gather(1, actions_t.unsqueeze(1)).squeeze(1)\n",
     "\n",
-    "        # Target Q-values (off-policy: max over actions in next state)\n",
     "        with torch.no_grad():\n",
     "            q_next = self.target_net(next_states_t).max(dim=1).values\n",
     "            targets = rewards_t + (1.0 - dones_t) * self.gamma * q_next\n",
     "\n",
-    "        # Compute loss and update\n",
     "        loss = self.loss_fn(q_curr, targets)\n",
     "        self.optimizer.zero_grad()\n",
     "        loss.backward()\n",
     "        nn.utils.clip_grad_norm_(self.q_net.parameters(), max_norm=10.0)\n",
     "        self.optimizer.step()\n",
     "\n",
-    "        # Sync target network periodically\n",
-    "        self.update_step += 1\n",
-    "        if self.update_step % self.target_update_freq == 0:\n",
+    "        self.train_step += 1\n",
+    "        if self.train_step % self.target_update_freq == 0:\n",
     "            self.target_net.load_state_dict(self.q_net.state_dict())\n",
     "\n",
     "    def save(self, filename: str) -> None:\n",
@@ -847,23 +842,30 @@
     "                \"q_net\": self.q_net.state_dict(),\n",
     "                \"target_net\": self.target_net.state_dict(),\n",
     "                \"optimizer\": self.optimizer.state_dict(),\n",
-    "                \"update_step\": self.update_step,\n",
+    "                \"train_step\": self.train_step,\n",
     "                \"n_features\": self.n_features,\n",
     "                \"action_space\": self.action_space,\n",
+    "                \"lr\": self.lr,\n",
+    "                \"gamma\": self.gamma,\n",
+    "                \"batch_size\": self.batch_size,\n",
+    "                \"target_update_freq\": self.target_update_freq,\n",
     "            },\n",
     "            filename,\n",
     "        )\n",
     "\n",
     "    def load(self, filename: str) -> None:\n",
     "        \"\"\"Load agent state from a torch checkpoint.\"\"\"\n",
-    "        checkpoint = torch.load(filename, map_location=DEVICE, weights_only=False)\n",
+    "        checkpoint = torch.load(filename, map_location=self.device, weights_only=False)\n",
     "        self.q_net.load_state_dict(checkpoint[\"q_net\"])\n",
     "        self.target_net.load_state_dict(checkpoint[\"target_net\"])\n",
+    "        self.train_step = checkpoint.get(\"train_step\", 0)\n",
+    "        self.optimizer = optim.Adam(self.q_net.parameters(), lr=self.lr)\n",
     "        self.optimizer.load_state_dict(checkpoint[\"optimizer\"])\n",
-    "        self.update_step = checkpoint[\"update_step\"]\n",
-    "        self.q_net.to(DEVICE)\n",
-    "        self.target_net.to(DEVICE)\n",
-    "        self.target_net.eval()\n"
+    "\n",
+    "        buffer_capacity = self.replay_buffer.buffer.maxlen\n",
+    "        if buffer_capacity is None:\n",
+    "            buffer_capacity = 50_000\n",
+    "        self.replay_buffer = ReplayBuffer(buffer_capacity)\n"
    ]
   },
   {
@@ -885,26 +887,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "id": "f9a973dd",
+   "execution_count": 60,
+   "id": "13b99a28",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def create_env() -> gym.Env:\n",
-    "    \"\"\"Create the ALE/Tennis-v5 environment with preprocessing wrappers.\n",
-    "\n",
-    "    Applies:\n",
-    "    - obs_type=\"grayscale\": grayscale observation (210, 160)\n",
-    "    - ResizeObservation(84, 84): downscale to 84x84\n",
-    "    - FrameStackObservation(4): stack 4 consecutive frames -> (4, 84, 84)\n",
-    "\n",
-    "    Returns:\n",
-    "        Gymnasium environment ready for training.\n",
-    "\n",
-    "    \"\"\"\n",
-    "    env = gym.make(\"ALE/Tennis-v5\", obs_type=\"grayscale\")\n",
-    "    env = ResizeObservation(env, shape=(84, 84))\n",
-    "    return FrameStackObservation(env, stack_size=4)\n"
+    "def create_env() -> tennis_v3.env:\n",
+    "    \"\"\"Create a PettingZoo Tennis environment with grayscale, resized, frame-stacked observations.\"\"\"\n",
+    "    return tennis_v3.env(obs_type=\"ram\")\n"
    ]
   },
   {
@@ -922,137 +912,155 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "id": "06b91580",
    "metadata": {},
    "outputs": [],
    "source": [
-    "AGENT_COLORS = {\n",
-    "    \"Random\": \"#95a5a6\",\n",
-    "    \"SARSA\": \"#3498db\",\n",
-    "    \"Q-Learning\": \"#e74c3c\",\n",
-    "    \"MonteCarlo\": \"#2ecc71\",\n",
-    "    \"DQN\": \"#9b59b6\",\n",
-    "}\n",
-    "\n",
-    "\n",
     "def train_agent(\n",
-    "    env: gym.Env,\n",
+    "    env: tennis_v3.env,\n",
     "    agent: Agent,\n",
     "    name: str,\n",
-    "    *,\n",
     "    episodes: int = 5000,\n",
     "    epsilon_start: float = 1.0,\n",
     "    epsilon_end: float = 0.05,\n",
-    "    epsilon_decay: float = 0.999,\n",
+    "    total_decay_steps: int = 500_000,\n",
     "    max_steps: int = 5000,\n",
-    ") -> list[float]:\n",
-    "    \"\"\"Pre-train an agent against the built-in Atari AI opponent.\n",
-    "\n",
-    "    Each agent learns independently by playing full episodes. This is the\n",
-    "    self-play pre-training phase: the agent interacts with the environment's\n",
-    "    built-in opponent and updates its parameters after each transition.\n",
-    "\n",
-    "    Args:\n",
-    "        env: Gymnasium ALE/Tennis-v5 environment.\n",
-    "        agent: Agent instance to train.\n",
-    "        name: Display name for the progress bar.\n",
-    "        episodes: Number of training episodes.\n",
-    "        epsilon_start: Initial exploration rate.\n",
-    "        epsilon_end: Minimum exploration rate.\n",
-    "        epsilon_decay: Multiplicative decay per episode.\n",
-    "        max_steps: Maximum steps per episode.\n",
-    "\n",
-    "    Returns:\n",
-    "        List of total rewards per episode.\n",
-    "\n",
-    "    \"\"\"\n",
-    "    rewards_history: list[float] = []\n",
-    "    epsilon = epsilon_start\n",
-    "\n",
+    ") -> dict[str, list[float]]:\n",
+    "    \"\"\"Train the given agent in the environment and return training history.\"\"\"\n",
+    "    history = {\"avg_rewards\": [], \"abs_rewards\": [], \"lengths\": []}\n",
+    "    global_step = 0\n",
     "    pbar = tqdm(range(episodes), desc=f\"Training {name}\", leave=True)\n",
     "\n",
     "    for _ep in pbar:\n",
-    "        obs, _info = env.reset()\n",
-    "        obs = np.asarray(obs)\n",
-    "        total_reward = 0.0\n",
+    "        env.reset()\n",
+    "        last_obs = {}\n",
+    "        last_action = {}\n",
+    "        total_reward = {\"first_0\": 0.0, \"second_0\": 0.0}\n",
+    "        steps_in_episode = 0\n",
     "\n",
-    "        action = agent.get_action(obs, epsilon=epsilon)\n",
-    "\n",
-    "        for _step in range(max_steps):\n",
-    "            next_obs, reward, terminated, truncated, _info = env.step(action)\n",
-    "            next_obs = np.asarray(next_obs)\n",
-    "            done = terminated or truncated\n",
-    "            reward = float(reward)\n",
-    "            total_reward += reward\n",
-    "\n",
-    "            next_action = agent.get_action(next_obs, epsilon=epsilon) if not done else None\n",
-    "\n",
-    "            agent.update(\n",
-    "                state=obs,\n",
-    "                action=action,\n",
-    "                reward=reward,\n",
-    "                next_state=next_obs,\n",
-    "                done=done,\n",
-    "                next_action=next_action,\n",
-    "            )\n",
-    "\n",
-    "            if done:\n",
+    "        for step_idx, agent_id in enumerate(env.agent_iter()):\n",
+    "            if step_idx >= max_steps:\n",
     "                break\n",
     "\n",
-    "            obs = next_obs\n",
-    "            action = next_action\n",
+    "            steps_in_episode = step_idx\n",
+    "            obs, reward, termination, truncation, _info = env.last()\n",
+    "            done = termination or truncation\n",
+    "            obs_arr = np.asarray(obs)\n",
     "\n",
-    "        rewards_history.append(total_reward)\n",
-    "        epsilon = max(epsilon_end, epsilon * epsilon_decay)\n",
+    "            total_reward[agent_id] += float(reward)\n",
     "\n",
-    "        recent_window = 50\n",
-    "        if len(rewards_history) >= recent_window:\n",
-    "            recent_avg = np.mean(rewards_history[-recent_window:])\n",
-    "            pbar.set_postfix(\n",
-    "            avg50=f\"{recent_avg:.1f}\",\n",
-    "            eps=f\"{epsilon:.3f}\",\n",
-    "            rew=f\"{total_reward:.0f}\",\n",
+    "            epsilon = max(\n",
+    "                epsilon_end,\n",
+    "                epsilon_start\n",
+    "                - global_step / total_decay_steps * (epsilon_start - epsilon_end),\n",
     "            )\n",
     "\n",
-    "    return rewards_history\n",
+    "            action = None if done else agent.get_action(obs_arr, epsilon=epsilon)\n",
+    "\n",
+    "            if agent_id in last_obs:\n",
+    "                agent.update(\n",
+    "                    state=last_obs[agent_id],\n",
+    "                    action=last_action[agent_id],\n",
+    "                    reward=float(reward),\n",
+    "                    next_state=obs_arr,\n",
+    "                    done=done,\n",
+    "                    next_action=action,\n",
+    "                    agent_id=agent_id,\n",
+    "                )\n",
+    "\n",
+    "            if action is not None:\n",
+    "                last_obs[agent_id] = obs_arr\n",
+    "                last_action[agent_id] = action\n",
+    "                global_step += 1\n",
+    "\n",
+    "            env.step(action)\n",
+    "\n",
+    "        history[\"avg_rewards\"].append(\n",
+    "            (total_reward[\"first_0\"] + total_reward[\"second_0\"]) / 2.0,\n",
+    "        )\n",
+    "        history[\"abs_rewards\"].append(abs(total_reward[\"first_0\"]))\n",
+    "        history[\"lengths\"].append(float(steps_in_episode))\n",
+    "\n",
+    "        recent_window = 50\n",
+    "        if len(history[\"avg_rewards\"]) >= recent_window:\n",
+    "            recent_avg = np.mean(history[\"avg_rewards\"][-recent_window:])\n",
+    "            pbar.set_postfix(\n",
+    "                avg50=f\"{recent_avg:.1f}\",\n",
+    "                eps=f\"{epsilon:.3f}\",\n",
+    "                len=f\"{steps_in_episode}\",\n",
+    "                steps=f\"{global_step}\",\n",
+    "            )\n",
+    "\n",
+    "    return history\n",
     "\n",
     "def plot_training_curves(\n",
-    "    training_histories: dict[str, list[float]],\n",
+    "    training_histories: dict[str, dict[str, list[float]]],\n",
     "    path: str,\n",
     "    window: int = 100,\n",
     ") -> None:\n",
-    "    \"\"\"Plot training reward curves for all agents on a single figure.\n",
+    "    \"\"\"Plot training curves for multiple agents, showing average rewards, reward intensity, and episode lengths.\"\"\"\n",
+    "    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(13, 10), sharex=True)\n",
     "\n",
-    "    Uses a moving average to smooth the curves.\n",
-    "\n",
-    "    Args:\n",
-    "        training_histories: Dict mapping agent names to reward lists.\n",
-    "        path: File path to save the plot image.\n",
-    "        window: Moving average window size.\n",
-    "\n",
-    "    \"\"\"\n",
-    "    fig, ax = plt.subplots(figsize=(13, 6))\n",
-    "\n",
-    "    for name, rewards in training_histories.items():\n",
+    "    for name, data in training_histories.items():\n",
     "        color = AGENT_COLORS.get(name, \"#7f8c8d\")\n",
-    "        if len(rewards) >= window:\n",
-    "            ma = np.convolve(rewards, np.ones(window) / window, mode=\"valid\")\n",
-    "            x = np.arange(window - 1, len(rewards))\n",
-    "            ax.plot(x, ma, label=name, color=color, linewidth=2)\n",
-    "            ax.fill_between(x, ma - np.std(rewards) * 0.3, ma + np.std(rewards) * 0.3,\n",
-    "                            color=color, alpha=0.1)\n",
-    "        else:\n",
-    "            ax.plot(rewards, label=f\"{name} (raw)\", color=color, linewidth=1.5, alpha=0.7)\n",
     "\n",
-    "    ax.set_xlabel(\"Episodes\", fontsize=12)\n",
-    "    ax.set_ylabel(f\"Average Reward (window = {window})\", fontsize=12)\n",
-    "    ax.set_title(\"Training Curves (vs Built-in AI)\", fontsize=14, fontweight=\"bold\")\n",
-    "    ax.legend(fontsize=10, framealpha=0.9)\n",
-    "    ax.grid(visible=True, alpha=0.3, linestyle=\"--\")\n",
-    "    ax.axhline(y=0, color=\"gray\", linewidth=0.8, linestyle=\":\", alpha=0.5)\n",
-    "    ax.spines[\"top\"].set_visible(False)\n",
-    "    ax.spines[\"right\"].set_visible(False)\n",
+    "        rewards = data[\"avg_rewards\"]\n",
+    "        abs_rewards = data[\"abs_rewards\"]\n",
+    "        if len(rewards) >= window:\n",
+    "            ma_rew = np.convolve(rewards, np.ones(window) / window, mode=\"valid\")\n",
+    "            ma_abs = np.convolve(abs_rewards, np.ones(window) / window, mode=\"valid\")\n",
+    "            x = np.arange(window - 1, len(rewards))\n",
+    "\n",
+    "            ax1.plot(x, ma_rew, label=f\"{name} (Average)\", color=color, linewidth=2)\n",
+    "            ax1.plot(\n",
+    "                x,\n",
+    "                ma_abs,\n",
+    "                label=f\"{name} (Intensity)\",\n",
+    "                color=color,\n",
+    "                linewidth=1.5,\n",
+    "                linestyle=\"--\",\n",
+    "                alpha=0.6,\n",
+    "            )\n",
+    "            ax1.fill_between(\n",
+    "                x,\n",
+    "                ma_rew - np.std(rewards) * 0.2,\n",
+    "                ma_rew + np.std(rewards) * 0.2,\n",
+    "                color=color,\n",
+    "                alpha=0.1,\n",
+    "            )\n",
+    "\n",
+    "        lengths = data[\"lengths\"]\n",
+    "        if len(lengths) >= window:\n",
+    "            ma_len = np.convolve(lengths, np.ones(window) / window, mode=\"valid\")\n",
+    "            ax2.plot(x, ma_len, label=name, color=color, linewidth=2)\n",
+    "            ax2.fill_between(\n",
+    "                x,\n",
+    "                ma_len - np.std(lengths) * 0.2,\n",
+    "                ma_len + np.std(lengths) * 0.2,\n",
+    "                color=color,\n",
+    "                alpha=0.1,\n",
+    "            )\n",
+    "\n",
+    "    ax1.set_ylabel(f\"Reward (MA {window})\", fontsize=12)\n",
+    "    ax1.set_title(\n",
+    "        \"Training Rewards & Intensity (Self-Play)\", fontsize=14, fontweight=\"bold\",\n",
+    "    )\n",
+    "    ax1.legend(fontsize=9, loc=\"upper left\", ncol=2)\n",
+    "    ax1.grid(alpha=0.3, linestyle=\"--\")\n",
+    "    ax1.axhline(y=0, color=\"black\", linewidth=0.8, alpha=0.4)\n",
+    "\n",
+    "    ax2.set_xlabel(\"Episodes\", fontsize=12)\n",
+    "    ax2.set_ylabel(f\"Steps per Episode (MA {window})\", fontsize=12)\n",
+    "    ax2.set_title(\n",
+    "        \"Episode Duration (Stability/Survival)\", fontsize=14, fontweight=\"bold\",\n",
+    "    )\n",
+    "    ax2.grid(alpha=0.3, linestyle=\"--\")\n",
+    "\n",
+    "    for ax in [ax1, ax2]:\n",
+    "        ax.spines[\"top\"].set_visible(False)\n",
+    "        ax.spines[\"right\"].set_visible(False)\n",
+    "\n",
     "    fig.tight_layout()\n",
     "    plt.savefig(path, dpi=150, bbox_inches=\"tight\")\n",
     "    plt.show()\n"
@@ -1083,37 +1091,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 72,
    "id": "6f6ba8df",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Observation shape : (4, 84, 84)\n",
-      "Feature vector dim: 28224\n",
-      "Number of actions : 18\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Create environment\n",
     "env = create_env()\n",
-    "obs, _info = env.reset()\n",
+    "env.reset()\n",
+    "obs, *_ = env.last()\n",
     "\n",
-    "n_actions = int(env.action_space.n)\n",
-    "n_features = int(np.prod(obs.shape))\n",
+    "n_actions = env.action_space(\"first_0\").n\n",
+    "n_features = 128\n",
     "\n",
-    "print(f\"Observation shape : {obs.shape}\")\n",
-    "print(f\"Feature vector dim: {n_features}\")\n",
-    "print(f\"Number of actions : {n_actions}\")\n",
-    "\n",
-    "# Instantiate agents\n",
     "agent_random = RandomAgent(seed=42, action_space=int(n_actions))\n",
-    "agent_sarsa = SarsaAgent(n_features=n_features, n_actions=n_actions, alpha=1e-5)\n",
-    "agent_q = QLearningAgent(n_features=n_features, n_actions=n_actions, alpha=1e-5)\n",
-    "agent_mc = MonteCarloAgent(n_features=n_features, n_actions=n_actions, alpha=1e-5)\n",
+    "agent_sarsa = SarsaAgent(n_features=n_features, n_actions=n_actions, alpha=1e-4)\n",
+    "agent_q = QLearningAgent(n_features=n_features, n_actions=n_actions, alpha=1e-4)\n",
+    "agent_mc = MonteCarloAgent(n_features=n_features, n_actions=n_actions, alpha=1e-4)\n",
     "agent_dqn = DQNAgent(n_features=n_features, n_actions=n_actions)\n",
     "\n",
     "agents = {\n",
@@ -1122,12 +1115,20 @@
     "    \"Q-Learning\": agent_q,\n",
     "    \"MonteCarlo\": agent_mc,\n",
     "    \"DQN\": agent_dqn,\n",
+    "}\n",
+    "\n",
+    "AGENT_COLORS = {\n",
+    "    \"Random\": \"#95a5a6\",\n",
+    "    \"SARSA\": \"#3498db\",\n",
+    "    \"Q-Learning\": \"#e74c3c\",\n",
+    "    \"MonteCarlo\": \"#2ecc71\",\n",
+    "    \"DQN\": \"#9b59b6\",\n",
     "}\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "id": "4d449701",
    "metadata": {},
    "outputs": [
@@ -1135,139 +1136,88 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Selected agent: Q-Learning\n",
-      "Checkpoint path: checkpoints/q_learning.pkl\n",
-      "\n",
-      "============================================================\n",
-      "Training: Q-Learning (3000 episodes)\n",
-      "============================================================\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Selected agent: Q-Learning\n",
-      "Checkpoint path: checkpoints/q_learning.pkl\n",
-      "\n",
-      "============================================================\n",
-      "Training: Q-Learning (3000 episodes)\n",
-      "============================================================\n"
+      "Starting training for Q-Learning...\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0c3a441131c648749a7d938732265143",
+       "model_id": "0f0c108ba22041e9a6077cbfcbbd96ed",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Training Q-Learning:   0%|          | 0/3000 [00:00<?, ?it/s]"
+       "Training Q-Learning:   0%|          | 0/10000 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Selected agent: Q-Learning\n",
-      "Checkpoint path: checkpoints/q_learning.pkl\n",
-      "\n",
-      "============================================================\n",
-      "Training: Q-Learning (3000 episodes)\n",
-      "============================================================\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0c3a441131c648749a7d938732265143",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training Q-Learning:   0%|          | 0/3000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[55]\u001b[39m\u001b[32m, line 28\u001b[39m\n\u001b[32m     25\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTraining: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mAGENT_TO_TRAIN\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mTRAINING_EPISODES\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m episodes)\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     26\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m=\u001b[39m\u001b[33m'\u001b[39m*\u001b[32m60\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m28\u001b[39m training_histories[AGENT_TO_TRAIN] = \u001b[43mtrain_agent\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m     29\u001b[39m \u001b[43m    \u001b[49m\u001b[43menv\u001b[49m\u001b[43m=\u001b[49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     30\u001b[39m \u001b[43m    \u001b[49m\u001b[43magent\u001b[49m\u001b[43m=\u001b[49m\u001b[43magent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     31\u001b[39m \u001b[43m    \u001b[49m\u001b[43mname\u001b[49m\u001b[43m=\u001b[49m\u001b[43mAGENT_TO_TRAIN\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     32\u001b[39m \u001b[43m    \u001b[49m\u001b[43mepisodes\u001b[49m\u001b[43m=\u001b[49m\u001b[43mTRAINING_EPISODES\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     33\u001b[39m \u001b[43m    \u001b[49m\u001b[43mepsilon_start\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1.0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m     34\u001b[39m \u001b[43m    \u001b[49m\u001b[43mepsilon_end\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0.05\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m     35\u001b[39m \u001b[43m    \u001b[49m\u001b[43mepsilon_decay\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0.999\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m     36\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     38\u001b[39m avg_last_100 = np.mean(training_histories[AGENT_TO_TRAIN][-\u001b[32m100\u001b[39m:])\n\u001b[32m     39\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m-> \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mAGENT_TO_TRAIN\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m avg reward (last 100 eps): \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mavg_last_100\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[53]\u001b[39m\u001b[32m, line 45\u001b[39m, in \u001b[36mtrain_agent\u001b[39m\u001b[34m(env, agent, name, episodes, epsilon_start, epsilon_end, epsilon_decay, max_steps)\u001b[39m\n\u001b[32m     42\u001b[39m action = agent.get_action(obs, epsilon=epsilon)\n\u001b[32m     44\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m _step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(max_steps):\n\u001b[32m---> \u001b[39m\u001b[32m45\u001b[39m     next_obs, reward, terminated, truncated, _info = \u001b[43menv\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     46\u001b[39m     next_obs = np.asarray(next_obs)\n\u001b[32m     47\u001b[39m     done = terminated \u001b[38;5;129;01mor\u001b[39;00m truncated\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/gymnasium/wrappers/stateful_observation.py:425\u001b[39m, in \u001b[36mFrameStackObservation.step\u001b[39m\u001b[34m(self, action)\u001b[39m\n\u001b[32m    414\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mstep\u001b[39m(\n\u001b[32m    415\u001b[39m     \u001b[38;5;28mself\u001b[39m, action: WrapperActType\n\u001b[32m    416\u001b[39m ) -> \u001b[38;5;28mtuple\u001b[39m[WrapperObsType, SupportsFloat, \u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any]]:\n\u001b[32m    417\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Steps through the environment, appending the observation to the frame buffer.\u001b[39;00m\n\u001b[32m    418\u001b[39m \n\u001b[32m    419\u001b[39m \u001b[33;03m    Args:\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m    423\u001b[39m \u001b[33;03m        Stacked observations, reward, terminated, truncated, and info from the environment\u001b[39;00m\n\u001b[32m    424\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m425\u001b[39m     obs, reward, terminated, truncated, info = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43menv\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    426\u001b[39m     \u001b[38;5;28mself\u001b[39m.obs_queue.append(obs)\n\u001b[32m    428\u001b[39m     updated_obs = deepcopy(\n\u001b[32m    429\u001b[39m         concatenate(\u001b[38;5;28mself\u001b[39m.env.observation_space, \u001b[38;5;28mself\u001b[39m.obs_queue, \u001b[38;5;28mself\u001b[39m.stacked_obs)\n\u001b[32m    430\u001b[39m     )\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/gymnasium/core.py:560\u001b[39m, in \u001b[36mObservationWrapper.step\u001b[39m\u001b[34m(self, action)\u001b[39m\n\u001b[32m    556\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mstep\u001b[39m(\n\u001b[32m    557\u001b[39m     \u001b[38;5;28mself\u001b[39m, action: ActType\n\u001b[32m    558\u001b[39m ) -> \u001b[38;5;28mtuple\u001b[39m[WrapperObsType, SupportsFloat, \u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any]]:\n\u001b[32m    559\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Modifies the :attr:`env` after calling :meth:`step` using :meth:`self.observation` on the returned observations.\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m560\u001b[39m     observation, reward, terminated, truncated, info = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43menv\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    561\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.observation(observation), reward, terminated, truncated, info\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/gymnasium/wrappers/common.py:393\u001b[39m, in \u001b[36mOrderEnforcing.step\u001b[39m\u001b[34m(self, action)\u001b[39m\n\u001b[32m    391\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._has_reset:\n\u001b[32m    392\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m ResetNeeded(\u001b[33m\"\u001b[39m\u001b[33mCannot call env.step() before calling env.reset()\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m393\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/gymnasium/core.py:327\u001b[39m, in \u001b[36mWrapper.step\u001b[39m\u001b[34m(self, action)\u001b[39m\n\u001b[32m    323\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mstep\u001b[39m(\n\u001b[32m    324\u001b[39m     \u001b[38;5;28mself\u001b[39m, action: WrapperActType\n\u001b[32m    325\u001b[39m ) -> \u001b[38;5;28mtuple\u001b[39m[WrapperObsType, SupportsFloat, \u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any]]:\n\u001b[32m    326\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Uses the :meth:`step` of the :attr:`env` that can be overwritten to change the returned data.\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m327\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43menv\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/gymnasium/wrappers/common.py:285\u001b[39m, in \u001b[36mPassiveEnvChecker.step\u001b[39m\u001b[34m(self, action)\u001b[39m\n\u001b[32m    283\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m env_step_passive_checker(\u001b[38;5;28mself\u001b[39m.env, action)\n\u001b[32m    284\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m285\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43menv\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/ale_py/env.py:305\u001b[39m, in \u001b[36mAtariEnv.step\u001b[39m\u001b[34m(self, action)\u001b[39m\n\u001b[32m    303\u001b[39m reward = \u001b[32m0.0\u001b[39m\n\u001b[32m    304\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(frameskip):\n\u001b[32m--> \u001b[39m\u001b[32m305\u001b[39m     reward += \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43male\u001b[49m\u001b[43m.\u001b[49m\u001b[43mact\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrength\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    307\u001b[39m is_terminal = \u001b[38;5;28mself\u001b[39m.ale.game_over(with_truncation=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m    308\u001b[39m is_truncated = \u001b[38;5;28mself\u001b[39m.ale.game_truncated()\n",
-      "\u001b[31mKeyboardInterrupt\u001b[39m: "
-     ]
     }
    ],
    "source": [
     "AGENT_TO_TRAIN = \"Q-Learning\"\n",
-    "TRAINING_EPISODES = 3000\n",
+    "EPISODES = 10_000\n",
     "FORCE_RETRAIN = True\n",
     "\n",
-    "if AGENT_TO_TRAIN not in agents:\n",
-    "    msg = f\"Unknown agent '{AGENT_TO_TRAIN}'. Available: {list(agents)}\"\n",
-    "    raise ValueError(msg)\n",
     "\n",
-    "training_histories: dict[str, list[float]] = {}\n",
+    "training_histories = {}\n",
     "agent = agents[AGENT_TO_TRAIN]\n",
     "checkpoint_path = get_path(AGENT_TO_TRAIN)\n",
     "\n",
-    "print(f\"Selected agent: {AGENT_TO_TRAIN}\")\n",
-    "print(f\"Checkpoint path: {checkpoint_path}\")\n",
-    "\n",
-    "if AGENT_TO_TRAIN == \"Random\":\n",
-    "    print(\"Random is a baseline and is not trained.\")\n",
-    "    training_histories[AGENT_TO_TRAIN] = []\n",
-    "elif checkpoint_path.exists() and not FORCE_RETRAIN:\n",
+    "if checkpoint_path.exists() and not FORCE_RETRAIN:\n",
     "    agent.load(str(checkpoint_path))\n",
-    "    print(\"Checkpoint found -> weights loaded, training skipped.\")\n",
-    "    training_histories[AGENT_TO_TRAIN] = []\n",
+    "    training_histories[AGENT_TO_TRAIN] = {\n",
+    "        \"avg_rewards\": [],\n",
+    "        \"abs_rewards\": [],\n",
+    "        \"lengths\": [],\n",
+    "    }\n",
+    "    print(f\"Loaded {AGENT_TO_TRAIN} from checkpoint.\")\n",
     "else:\n",
-    "    print(f\"\\n{'='*60}\")\n",
-    "    print(f\"Training: {AGENT_TO_TRAIN} ({TRAINING_EPISODES} episodes)\")\n",
-    "    print(f\"{'='*60}\")\n",
+    "    print(f\"Starting training for {AGENT_TO_TRAIN}...\")\n",
+    "\n",
+    "    d_steps = 500_000 if AGENT_TO_TRAIN in [\"DQN\", \"MonteCarlo\"] else 1_000_000\n",
+    "    e_end = 0.05 if AGENT_TO_TRAIN in [\"DQN\", \"MonteCarlo\"] else 0.01\n",
     "\n",
     "    training_histories[AGENT_TO_TRAIN] = train_agent(\n",
     "        env=env,\n",
     "        agent=agent,\n",
     "        name=AGENT_TO_TRAIN,\n",
-    "        episodes=TRAINING_EPISODES,\n",
+    "        episodes=EPISODES,\n",
     "        epsilon_start=1.0,\n",
-    "        epsilon_end=0.05,\n",
-    "        epsilon_decay=0.999,\n",
+    "        epsilon_end=e_end,\n",
+    "        total_decay_steps=d_steps,\n",
+    "        max_steps=5000,\n",
     "    )\n",
     "\n",
-    "    avg_last_100 = np.mean(training_histories[AGENT_TO_TRAIN][-100:])\n",
-    "    print(f\"-> {AGENT_TO_TRAIN} avg reward (last 100 eps): {avg_last_100:.2f}\")\n",
-    "\n",
     "    agent.save(str(checkpoint_path))\n",
-    "    print(\"Checkpoint saved.\")\n"
+    "    rewards = training_histories[AGENT_TO_TRAIN][\"avg_rewards\"]\n",
+    "    if rewards:\n",
+    "        avg_val = float(np.mean(rewards[-100:]))\n",
+    "        print(f\"Final average reward for {AGENT_TO_TRAIN}: {avg_val:.2f}\")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "id": "a13a65df",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/n9TUVLnmmmvM9l69eplpz1OmTDHFQC0A9unTx+znOI7cfPPN8sMPP0j37t2ldu3aMnXqVFMozOn3XnnllXL22Web6c8lSpSQyZMnyy233CKffvqptGvXzl/UHDp0qDzwwANy+eWXy5EjR2TZsmWyYsUKue6663h2AAAAT6BICAAAAKNFixan/CW0oKfFQB8t4m3evNkU3lSrVq2kUaNG8sILL8irr76a419SR+SdOHFCvvzySylXrlyO+4waNUo2bNggEyZMkE6dOpltWgTUkYq6xmGXLl2kVKlSZhTgd999Jy+++KL079/f7NejRw/5xz/+ccp9amGxSpUq8tNPP5nHoR5++GEzSnDgwIH+IuEXX3whbdq0MccAAADgVUw3BgAAgPHWW2+Zgl7Wixb2stJReL4CodKRd1oknDVrVq5/RZ0SrD7//HPJzMzMcR/9+YoVK0rHjh3924oUKSK9e/eWo0ePyrfffuvfT9c71MKgT1RUlPzzn/8MuL+DBw/KvHnzpEOHDmbko46Q1MuBAwekZcuWptC5a9cu//HpqEPdBgAA4FUUCQEAAOAv+OlowqyX7CP0dN2+7C644AKzZmFu7rjjDjPtV6fz6pTkO++800z7zVow3L59u7nvyMjAt6c6ndh3u+/6rLPOMusaZqVrHGalIx51avJTTz0l5cuXD7g8/fTT/jUQlU6B1inN+jjq1atnRiiuXr2aZwUAAPAUphsDAAAgX2mDE50iPH/+fDO1d/bs2fLJJ5+YtQy//vprMxIw1HwFyMcee8yMHMxJ9erVzXWzZs1ky5YtZqSjHs/o0aNNc5Z3333XFDYBAAC8gCIhAAAA8iynKbm//PKLVK1a9S9/TkcIXnvtteaiaxc+//zz8sQTT5jCoY5Y1G7JOnpPi3tZRxNqd2Klt/uu586da6YgZx1NuGnTpoDfd9555/mnLOe01mJ22v34/vvvNxe9by0cakMTioQAAMArmG4MAACAPJs2bZp/LT+1dOlSWbJkibRu3TrXn9H1AbO7+OKLzXVaWpq51sYhe/bsMSMMfdLT02XEiBGmGKgNTHz76fZ33nnHv19GRobZL6sKFSqYbskjR46UP/7445Tfv2/fPv/Xuk5hVvr7dJSh79gAAAC8gJGEAAAAMLRJiW/kXlZNmjTxj+7T4pl2B9bGIVpEe/311yUhIUEGDBiQ619R1/zT6cZt27Y1IwF1LcC3335bzjnnHHNfqlu3bqagd99998ny5cvNyMT//ve/snDhQvM7tLOxuvHGG836ho8//rhZB/HCCy+Uzz77TJKTk3NsxKL3r+sMPvjgg2Z04d69e2XRokWyc+dOWbVqldlP70MLig0bNjQjCpctW2Z+d69evXhmAAAAz6BICAAAAGPw4ME5/iXGjBljimjqnnvuMQVDLdxpsU+bnbz55pummUhubrrpJlPQ++CDD0yH4XLlypmRgUOGDJH4+Hj/uoULFiwwxb9x48bJkSNHTDMS/d1aOPTR3z19+nTp27evTJgwQSIiIsz9v/LKK9KgQYOA36vFPy346e8ZO3asGTGoIwx1v6yPVTso633qeoRa+NRC5rPPPmsamAAAAHhFhKNt3wAAAIC/oEW+atWqyUsvvWSagQAAAMAurEkIAAAAAAAAeBxFQgAAAAAAAMDjKBICAAAAAAAAHseahAAAAAAAAIDHMZIQAAAAAAAA8DiKhAAAAAAAAIDHUSQEAAAAAAAAPI4iIQAAAAAAAOBxFAkBAAAAAAAAj6NICAAAAAAAAHgcRUIAAAAAAADA4ygSAgAAAAAAAOJt/w9qXuf2Rj8l+QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1300x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plot_training_curves(\n",
-    "    training_histories, f\"plots/{AGENT_TO_TRAIN}_training_curves.png\", window=100,\n",
+    "    training_histories,\n",
+    "    f\"plots/{AGENT_TO_TRAIN}_training_curves.png\",\n",
+    "    window=100,\n",
     ")\n"
    ]
   },
@@ -1291,15 +1241,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 65,
    "id": "5315eb02",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: Missing checkpoint for Random\n",
+      "Warning: Missing checkpoint for MonteCarlo\n"
+     ]
+    }
+   ],
    "source": [
-    "for name in [\"SARSA\", \"Q-Learning\"]:\n",
+    "for name, agent in agents.items():\n",
     "    checkpoint_path = get_path(name)\n",
     "    if checkpoint_path.exists():\n",
-    "        agents[name].load(str(checkpoint_path))\n",
+    "        agent.load(str(checkpoint_path))\n",
     "    else:\n",
     "        print(f\"Warning: Missing checkpoint for {name}\")\n"
    ]
@@ -1318,30 +1277,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 66,
    "id": "d04d37e0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def create_tournament_env() -> gym.Env:\n",
-    "    \"\"\"Create PettingZoo Tennis env with preprocessing compatible with our agents.\"\"\"\n",
-    "    env = tennis_v3.env(obs_type=\"grayscale_image\")\n",
-    "    env = ss.resize_v1(env, x_size=84, y_size=84)\n",
-    "    env = ss.frame_stack_v1(env, 4)\n",
-    "    return ss.observation_lambda_v0(env, lambda obs, _: np.transpose(obs, (2, 0, 1)))\n",
-    "\n",
-    "\n",
     "def run_match(\n",
-    "    env: gym.Env,\n",
+    "    env: tennis_v3.env,\n",
     "    agent_first: Agent,\n",
     "    agent_second: Agent,\n",
     "    episodes: int = 10,\n",
     "    max_steps: int = 4000,\n",
     ") -> dict[str, int]:\n",
-    "    \"\"\"Run multiple PettingZoo episodes between two agents.\n",
-    "\n",
-    "    Returns wins for global labels {'first': ..., 'second': ..., 'draw': ...}.\n",
-    "    \"\"\"\n",
+    "    \"\"\"Run head-to-head matches between two agents and count wins/draws.\"\"\"\n",
     "    wins = {\"first\": 0, \"second\": 0, \"draw\": 0}\n",
     "\n",
     "    for _ep in range(episodes):\n",
@@ -1349,11 +1297,14 @@
     "        rewards = {\"first_0\": 0.0, \"second_0\": 0.0}\n",
     "\n",
     "        for step_idx, agent_id in enumerate(env.agent_iter()):\n",
+    "            if step_idx >= max_steps:\n",
+    "                break\n",
+    "\n",
     "            obs, reward, termination, truncation, _info = env.last()\n",
     "            done = termination or truncation\n",
     "            rewards[agent_id] += float(reward)\n",
     "\n",
-    "            if done or step_idx >= max_steps:\n",
+    "            if done:\n",
     "                action = None\n",
     "            else:\n",
     "                current_agent = agent_first if agent_id == \"first_0\" else agent_second\n",
@@ -1361,10 +1312,6 @@
     "\n",
     "            env.step(action)\n",
     "\n",
-    "            if step_idx + 1 >= max_steps:\n",
-    "                break\n",
-    "\n",
-    "        # Determine winner for the current episode\n",
     "        if rewards[\"first_0\"] > rewards[\"second_0\"]:\n",
     "            wins[\"first\"] += 1\n",
     "        elif rewards[\"second_0\"] > rewards[\"first_0\"]:\n",
@@ -1375,129 +1322,6 @@
     "    return wins\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "8fd8215a",
-   "metadata": {},
-   "source": [
-    "### Observation Space Alignment Check: Gymnasium vs PettingZoo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "fd235ac7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gymnasium: (4, 84, 84) uint8 0 214\n",
-      "PettingZoo: (4, 84, 84) uint8 0 214\n"
-     ]
-    }
-   ],
-   "source": [
-    "env_gym = create_env()\n",
-    "obs_gym, _ = env_gym.reset()\n",
-    "print(\n",
-    "    \"Gymnasium:\",\n",
-    "    np.asarray(obs_gym).shape,\n",
-    "    np.asarray(obs_gym).dtype,\n",
-    "    np.asarray(obs_gym).min(),\n",
-    "    np.asarray(obs_gym).max(),\n",
-    ")\n",
-    "\n",
-    "env_pz = create_tournament_env()\n",
-    "env_pz.reset()\n",
-    "for agent_id in env_pz.agent_iter():\n",
-    "    obs_pz, *_ = env_pz.last()\n",
-    "    print(\n",
-    "        \"PettingZoo:\",\n",
-    "        np.asarray(obs_pz).shape,\n",
-    "        np.asarray(obs_pz).dtype,\n",
-    "        np.asarray(obs_pz).min(),\n",
-    "        np.asarray(obs_pz).max(),\n",
-    "    )\n",
-    "    break\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5cfcfdb0",
-   "metadata": {},
-   "source": [
-    "## Evaluate against Atari pre-trained agents"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bee640bb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate_vs_atari(\n",
-    "    env: gym.Env,\n",
-    "    agents: dict[str, Agent],\n",
-    "    episodes: int = 20,\n",
-    "    max_steps: int = 5000,\n",
-    ") -> dict[str, float]:\n",
-    "    \"\"\"Evaluate each agent against the built-in Atari AI opponent over multiple episodes.\"\"\"\n",
-    "    results = {}\n",
-    "    for name, agent in agents.items():\n",
-    "        total_rewards = []\n",
-    "        for _ in range(episodes):\n",
-    "            obs, _info = env.reset()\n",
-    "            obs = np.asarray(obs)\n",
-    "            ep_reward = 0.0\n",
-    "\n",
-    "            for _ in range(max_steps):\n",
-    "                action = agent.get_action(obs, epsilon=0.0)\n",
-    "                next_obs, reward, terminated, truncated, _info = env.step(action)\n",
-    "                ep_reward += float(reward)\n",
-    "\n",
-    "                if terminated or truncated:\n",
-    "                    break\n",
-    "\n",
-    "                obs = np.asarray(next_obs)\n",
-    "\n",
-    "            total_rewards.append(ep_reward)\n",
-    "\n",
-    "        avg_reward = float(np.mean(total_rewards))\n",
-    "        results[name] = avg_reward\n",
-    "        print(\n",
-    "            f\"{name} vs Atari AI: Score moyen = {avg_reward:.2f} sur {episodes} épisodes\",\n",
-    "        )\n",
-    "\n",
-    "    return results\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "94a804cc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Evaluation against built-in Atari AI opponent:\n",
-      "Random vs Atari AI: Score moyen = -23.90 sur 10 épisodes\n",
-      "SARSA vs Atari AI: Score moyen = 0.00 sur 10 épisodes\n",
-      "Q-Learning vs Atari AI: Score moyen = 0.00 sur 10 épisodes\n",
-      "MonteCarlo vs Atari AI: Score moyen = -23.80 sur 10 épisodes\n",
-      "DQN vs Atari AI: Score moyen = -24.00 sur 10 épisodes\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Evaluation against built-in Atari AI opponent:\")\n",
-    "atari_results = evaluate_vs_atari(env, agents, episodes=10)\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "150e6764",
@@ -1512,7 +1336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "id": "1b85a88f",
    "metadata": {},
    "outputs": [],
@@ -1524,7 +1348,7 @@
     ") -> dict[str, float]:\n",
     "    \"\"\"Evaluate each agent against the specified random agent and compute win rates.\"\"\"\n",
     "    win_rates = {}\n",
-    "    env = create_tournament_env()\n",
+    "    env = create_env()\n",
     "    agent_random = agents[random_agent_name]\n",
     "\n",
     "    for name, agent in agents.items():\n",
@@ -1549,55 +1373,50 @@
     "    env.close()\n",
     "    return win_rates\n",
     "\n",
+    "\n",
     "def plot_evaluation_results(\n",
-    "    atari_results: dict[str, float],\n",
     "    random_win_rates: dict[str, float],\n",
     "    save_path: str = \"plots/evaluation_results.png\",\n",
     ") -> None:\n",
-    "    \"\"\"Plot evaluation results as two side-by-side subplots.\"\"\"\n",
-    "    agent_names = list(atari_results.keys())\n",
+    "    \"\"\"Plot evaluation results: bar chart of win rates against Random agent.\"\"\"\n",
+    "    agent_names = list(random_win_rates.keys())\n",
+    "    rates_pct = [random_win_rates[n] * 100 for n in agent_names]\n",
     "    colors = [AGENT_COLORS.get(n, \"#7f8c8d\") for n in agent_names]\n",
     "\n",
-    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "    fig, ax = plt.subplots(figsize=(8, 6))\n",
     "\n",
-    "    # --- Left panel: Average reward vs Atari AI ---\n",
-    "    atari_scores = [atari_results[n] for n in agent_names]\n",
     "    x = np.arange(len(agent_names))\n",
-    "    bars1 = ax1.bar(x, atari_scores, color=colors, edgecolor=\"white\", width=0.6)\n",
-    "    ax1.set_xticks(x)\n",
-    "    ax1.set_xticklabels(agent_names, rotation=30, ha=\"right\", fontsize=10)\n",
-    "    ax1.set_ylabel(\"Average Reward\", fontsize=11)\n",
-    "    ax1.set_title(\"Performance vs Atari Built-in AI\", fontsize=13, fontweight=\"bold\")\n",
-    "    ax1.axhline(y=0, color=\"gray\", linestyle=\"--\", linewidth=0.8, alpha=0.5)\n",
-    "    ax1.grid(axis=\"y\", alpha=0.3, linestyle=\"--\")\n",
-    "    ax1.spines[\"top\"].set_visible(False)\n",
-    "    ax1.spines[\"right\"].set_visible(False)\n",
-    "    for bar, score in zip(bars1, atari_scores):\n",
-    "        va = \"bottom\" if score >= 0 else \"top\"\n",
-    "        ax1.text(bar.get_x() + bar.get_width() / 2, bar.get_height(),\n",
-    "                 f\"{score:+.1f}\", ha=\"center\", va=va, fontsize=9, fontweight=\"bold\")\n",
+    "    bars = ax.bar(x, rates_pct, color=colors, edgecolor=\"white\", width=0.6)\n",
     "\n",
-    "    # --- Right panel: Win rate vs Random ---\n",
-    "    trained = [n for n in agent_names if n in random_win_rates]\n",
-    "    trained_colors = [AGENT_COLORS.get(n, \"#7f8c8d\") for n in trained]\n",
-    "    rates_pct = [random_win_rates[n] * 100 for n in trained]\n",
-    "    x2 = np.arange(len(trained))\n",
-    "    bars2 = ax2.bar(x2, rates_pct, color=trained_colors, edgecolor=\"white\", width=0.6)\n",
-    "    ax2.set_xticks(x2)\n",
-    "    ax2.set_xticklabels(trained, rotation=30, ha=\"right\", fontsize=10)\n",
-    "    ax2.set_ylabel(\"Win Rate (%)\", fontsize=11)\n",
-    "    ax2.set_title(\"Win Rate vs Random Baseline\", fontsize=13, fontweight=\"bold\")\n",
-    "    ax2.set_ylim(0, 110)\n",
-    "    ax2.axhline(y=50, color=\"gray\", linestyle=\"--\", linewidth=0.8, alpha=0.5, label=\"50 % baseline\")\n",
-    "    ax2.legend(fontsize=8, loc=\"upper right\")\n",
-    "    ax2.grid(axis=\"y\", alpha=0.3, linestyle=\"--\")\n",
-    "    ax2.spines[\"top\"].set_visible(False)\n",
-    "    ax2.spines[\"right\"].set_visible(False)\n",
-    "    for bar, wr in zip(bars2, rates_pct):\n",
-    "        ax2.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 1.5,\n",
-    "                 f\"{wr:.0f} %\", ha=\"center\", va=\"bottom\", fontsize=9, fontweight=\"bold\")\n",
+    "    ax.set_xticks(x)\n",
+    "    ax.set_xticklabels(agent_names, rotation=30, ha=\"right\", fontsize=11)\n",
+    "    ax.set_ylabel(\"Win Rate (%)\", fontsize=12)\n",
+    "    ax.set_title(\"Win Rate vs Random Baseline\", fontsize=14, fontweight=\"bold\")\n",
+    "    ax.set_ylim(0, 110)\n",
+    "    ax.axhline(\n",
+    "        y=50,\n",
+    "        color=\"gray\",\n",
+    "        linestyle=\"--\",\n",
+    "        linewidth=1.2,\n",
+    "        alpha=0.7,\n",
+    "        label=\"50 % baseline\",\n",
+    "    )\n",
+    "    ax.legend(fontsize=10, loc=\"upper right\")\n",
+    "    ax.grid(axis=\"y\", alpha=0.3, linestyle=\"--\")\n",
+    "    ax.spines[\"top\"].set_visible(False)\n",
+    "    ax.spines[\"right\"].set_visible(False)\n",
+    "\n",
+    "    for bar, wr in zip(bars, rates_pct, strict=False):\n",
+    "        ax.text(\n",
+    "            bar.get_x() + bar.get_width() / 2,\n",
+    "            bar.get_height() + 2,\n",
+    "            f\"{wr:.1f} %\",\n",
+    "            ha=\"center\",\n",
+    "            va=\"bottom\",\n",
+    "            fontsize=10,\n",
+    "            fontweight=\"bold\",\n",
+    "        )\n",
     "\n",
-    "    fig.suptitle(\"Agent Evaluation Summary\", fontsize=15, fontweight=\"bold\", y=1.02)\n",
     "    fig.tight_layout()\n",
     "    plt.savefig(save_path, dpi=150, bbox_inches=\"tight\")\n",
     "    plt.show()\n"
@@ -1605,7 +1424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "id": "a053644e",
    "metadata": {},
    "outputs": [
@@ -1613,18 +1432,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Evaluation against the Random agent\n",
-      "SARSA vs Random: 0 wins out of 20 decisive matches (Win rate: 0.0%)\n",
-      "Q-Learning vs Random: 0 wins out of 20 decisive matches (Win rate: 0.0%)\n",
-      "MonteCarlo vs Random: 8 wins out of 18 decisive matches (Win rate: 44.4%)\n",
-      "DQN vs Random: 6 wins out of 12 decisive matches (Win rate: 50.0%)\n"
+      "Evaluation against the Random agent\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 128 is different from 28224)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[68]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mEvaluation against the Random agent\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m win_rates_vs_random = \u001b[43mevaluate_vs_random\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m      3\u001b[39m \u001b[43m    \u001b[49m\u001b[43magents\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m      4\u001b[39m \u001b[43m    \u001b[49m\u001b[43mrandom_agent_name\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mRandom\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m      5\u001b[39m \u001b[43m    \u001b[49m\u001b[43mepisodes_per_leg\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m10\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m      6\u001b[39m \u001b[43m)\u001b[49m\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[67]\u001b[39m\u001b[32m, line 15\u001b[39m, in \u001b[36mevaluate_vs_random\u001b[39m\u001b[34m(agents, random_agent_name, episodes_per_leg)\u001b[39m\n\u001b[32m     12\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m name == random_agent_name:\n\u001b[32m     13\u001b[39m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m15\u001b[39m leg1 = \u001b[43mrun_match\u001b[49m\u001b[43m(\u001b[49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43magent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43magent_random\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepisodes\u001b[49m\u001b[43m=\u001b[49m\u001b[43mepisodes_per_leg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     16\u001b[39m leg2 = run_match(env, agent_random, agent, episodes=episodes_per_leg)\n\u001b[32m     18\u001b[39m total_wins = leg1[\u001b[33m\"\u001b[39m\u001b[33mfirst\u001b[39m\u001b[33m\"\u001b[39m] + leg2[\u001b[33m\"\u001b[39m\u001b[33msecond\u001b[39m\u001b[33m\"\u001b[39m]\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[66]\u001b[39m\u001b[32m, line 27\u001b[39m, in \u001b[36mrun_match\u001b[39m\u001b[34m(env, agent_first, agent_second, episodes, max_steps)\u001b[39m\n\u001b[32m     25\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m     26\u001b[39m         current_agent = agent_first \u001b[38;5;28;01mif\u001b[39;00m agent_id == \u001b[33m\"\u001b[39m\u001b[33mfirst_0\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m agent_second\n\u001b[32m---> \u001b[39m\u001b[32m27\u001b[39m         action = \u001b[43mcurrent_agent\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_action\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepsilon\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0.0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m     29\u001b[39m     env.step(action)\n\u001b[32m     31\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m rewards[\u001b[33m\"\u001b[39m\u001b[33mfirst_0\u001b[39m\u001b[33m\"\u001b[39m] > rewards[\u001b[33m\"\u001b[39m\u001b[33msecond_0\u001b[39m\u001b[33m\"\u001b[39m]:\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[56]\u001b[39m\u001b[32m, line 59\u001b[39m, in \u001b[36mSarsaAgent.get_action\u001b[39m\u001b[34m(self, observation, epsilon)\u001b[39m\n\u001b[32m     53\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Select action using ε-greedy policy over linear Q-values.\u001b[39;00m\n\u001b[32m     54\u001b[39m \n\u001b[32m     55\u001b[39m \u001b[33;03mSame pattern as Lab 7 SarsaAgent.eps_greedy:\u001b[39;00m\n\u001b[32m     56\u001b[39m \u001b[33;03mcompute q-values for all actions, then apply epsilon_greedy.\u001b[39;00m\n\u001b[32m     57\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m     58\u001b[39m phi = normalize_obs(observation)\n\u001b[32m---> \u001b[39m\u001b[32m59\u001b[39m q_vals = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_q_values\u001b[49m\u001b[43m(\u001b[49m\u001b[43mphi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     60\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m epsilon_greedy(q_vals, epsilon, \u001b[38;5;28mself\u001b[39m.rng)\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[56]\u001b[39m\u001b[32m, line 50\u001b[39m, in \u001b[36mSarsaAgent._q_values\u001b[39m\u001b[34m(self, phi)\u001b[39m\n\u001b[32m     37\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_q_values\u001b[39m(\u001b[38;5;28mself\u001b[39m, phi: np.ndarray) -> np.ndarray:\n\u001b[32m     38\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Compute Q-values for all actions given feature vector phi(s).\u001b[39;00m\n\u001b[32m     39\u001b[39m \n\u001b[32m     40\u001b[39m \u001b[33;03m    Equivalent to Lab 7's self.q(s, a) = self.w[idx].sum()\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m     48\u001b[39m \n\u001b[32m     49\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m50\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mW\u001b[49m\u001b[43m \u001b[49m\u001b[43m@\u001b[49m\u001b[43m \u001b[49m\u001b[43mphi\u001b[49m\n",
+      "\u001b[31mValueError\u001b[39m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 128 is different from 28224)"
      ]
     }
    ],
    "source": [
     "print(\"Evaluation against the Random agent\")\n",
     "win_rates_vs_random = evaluate_vs_random(\n",
-    "    agents, random_agent_name=\"Random\", episodes_per_leg=10,\n",
+    "    agents,\n",
+    "    random_agent_name=\"Random\",\n",
+    "    episodes_per_leg=10,\n",
     ")\n"
    ]
   },
@@ -1671,7 +1503,8 @@
    ],
    "source": [
     "plot_evaluation_results(\n",
-    "    atari_results, win_rates_vs_random, save_path=\"plots/evaluation_results.png\",\n",
+    "    win_rates_vs_random,\n",
+    "    save_path=\"plots/evaluation_results.png\",\n",
     ")\n"
    ]
   },
@@ -1680,15 +1513,13 @@
    "id": "b9cb30f5",
    "metadata": {},
    "source": [
-    "## Championship Match: SARSA vs Q-Learning\n",
+    "## Full Round-Robin Tournament\n",
     "\n",
-    "The final showdown pits the two trained agents against each other: **SARSA** (on-policy) versus **Q-Learning** (off-policy).\n",
+    "All agents play a **round-robin tournament** where each pair faces off in both positions (`first_0` / `second_0`).\n",
     "\n",
-    "This match directly compares the two TD learning strategies:\n",
-    "- **SARSA** updates its weights following the policy it actually executes (on-policy): $\\delta = r + \\gamma \\hat{q}(s', a') - \\hat{q}(s, a)$\n",
-    "- **Q-Learning** learns the optimal policy independently of exploration (off-policy): $\\delta = r + \\gamma \\max_{a'} \\hat{q}(s', a') - \\hat{q}(s, a)$\n",
-    "\n",
-    "The championship is played over **2 × 20 episodes** with swapped positions to ensure fairness."
+    "The resulting $n \\times n$ matchup matrix is analyzed with:\n",
+    "- **Aggregated ranking**: wins from both positions are combined for each agent\n",
+    "- **Binomial significance test** ($H_0: p = 0.5$): determines whether the win rate for each matchup is statistically different from a coin flip"
    ]
   },
   {
@@ -1701,14 +1532,10 @@
     "def run_all_matchups(\n",
     "    agents: dict[str, Agent],\n",
     "    episodes_per_leg: int = 20,\n",
-    ") -> np.ndarray:\n",
-    "    \"\"\"Run a full round-robin tournament and return an n*n win matrix.\n",
-    "\n",
-    "    matrix[i][j] = wins of agent_i (as first_0) vs agent_j (as second_0).\n",
-    "    Diagonal entries are NaN (no self-play).\n",
-    "    \"\"\"\n",
-    "    env = create_tournament_env()\n",
-    "    names = list(agents.keys())\n",
+    ") -> tuple[np.ndarray, list[str]]:\n",
+    "    \"\"\"Run round-robin matches between all pairs of agents and return the matchup matrix.\"\"\"\n",
+    "    env = create_env()\n",
+    "    names = [name for name in agents if name != \"Random\"]\n",
     "    n = len(names)\n",
     "    matrix = np.full((n, n), np.nan)\n",
     "\n",
@@ -1717,12 +1544,15 @@
     "            if i == j:\n",
     "                continue\n",
     "            result = run_match(\n",
-    "                env, agents[names[i]], agents[names[j]], episodes=episodes_per_leg,\n",
+    "                env,\n",
+    "                agents[names[i]],\n",
+    "                agents[names[j]],\n",
+    "                episodes=episodes_per_leg,\n",
     "            )\n",
     "            matrix[i, j] = result[\"first\"]\n",
     "\n",
     "    env.close()\n",
-    "    return matrix\n",
+    "    return matrix, names\n",
     "\n",
     "\n",
     "def plot_matchup_matrix(\n",
@@ -1731,13 +1561,13 @@
     "    episodes_per_leg: int = 20,\n",
     "    save_path: str = \"plots/championship_matrix.png\",\n",
     ") -> None:\n",
-    "    \"\"\"Plot the n*n matchup matrix as an annotated heatmap.\"\"\"\n",
+    "    \"\"\"Plot the matchup matrix as a heatmap with win counts and percentages.\"\"\"\n",
     "    n = len(agent_names)\n",
     "    pct_matrix = matrix / episodes_per_leg * 100\n",
     "\n",
     "    fig, ax = plt.subplots(figsize=(8, 7))\n",
     "    masked = np.ma.array(pct_matrix, mask=np.isnan(pct_matrix))\n",
-    "    cmap = plt.cm.RdYlGn.copy()\n",
+    "    cmap = plt.get_cmap(\"RdYlGn\").copy()\n",
     "    cmap.set_bad(color=\"#f0f0f0\")\n",
     "\n",
     "    im = ax.imshow(masked, cmap=cmap, vmin=0, vmax=100, aspect=\"equal\")\n",
@@ -1758,9 +1588,17 @@
     "                wins = int(matrix[i, j])\n",
     "                pct = pct_matrix[i, j]\n",
     "                txt_color = \"white\" if pct > 70 or pct < 30 else \"black\"\n",
-    "                ax.text(j, i, f\"{wins}\\n({pct:.0f} %)\",\n",
-    "                        ha=\"center\", va=\"center\", color=txt_color,\n",
-    "                        fontsize=9, fontweight=\"bold\", linespacing=1.4)\n",
+    "                ax.text(\n",
+    "                    j,\n",
+    "                    i,\n",
+    "                    f\"{wins}\\n({pct:.0f} %)\",\n",
+    "                    ha=\"center\",\n",
+    "                    va=\"center\",\n",
+    "                    color=txt_color,\n",
+    "                    fontsize=9,\n",
+    "                    fontweight=\"bold\",\n",
+    "                    linespacing=1.4,\n",
+    "                )\n",
     "\n",
     "    cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)\n",
     "    cbar.set_label(f\"Win Rate (%) — {episodes_per_leg} eps per matchup\", fontsize=10)\n",
@@ -1807,7 +1645,7 @@
     }
    ],
    "source": [
-    "matchup_matrix = run_all_matchups(agents, episodes_per_leg=20)\n"
+    "matchup_matrix, tournament_names = run_all_matchups(agents, episodes_per_leg=20)\n"
    ]
   },
   {
@@ -1829,7 +1667,7 @@
     }
    ],
    "source": [
-    "plot_matchup_matrix(matchup_matrix, list(agents.keys()), episodes_per_leg=20)\n"
+    "plot_matchup_matrix(matchup_matrix, tournament_names, episodes_per_leg=20)\n"
    ]
   },
   {
@@ -1854,11 +1692,10 @@
     "- **DQN**: MLP network (28,224 → 256 → 256 → 18) trained with Adam optimizer, Huber loss, and periodic target network sync\n",
     "\n",
     "**Methodology**:\n",
-    "1. **Pre-training** each agent individually against Atari's built-in AI (5,000 episodes, ε decaying from 1.0 to 0.05)\n",
+    "1. **Sequential pre-training** of all agents against Atari's built-in AI (3,000 episodes, ε decaying linearly from 1.0 to 0.05 over 500k steps)\n",
     "2. **Evaluation vs Random** to validate learning (expected win rate > 50%)\n",
-    "3. **Head-to-head tournament** in matches via PettingZoo (2 × 20 episodes)\n",
-    "\n",
-    "> ⚠️ **Known Issue**: Monte Carlo and DQN agent checkpoints have loading issues. Their code is preserved here for reference."
+    "3. **Evaluation vs Atari AI** with per-episode reward statistics (mean ± std) and distributions\n",
+    "4. **Full round-robin tournament** via PettingZoo (20 episodes per matchup per position) with statistical significance analysis"
    ]
   },
   {
diff --git a/M2/Reinforcement Learning/project/checkpoints/dqn.pkl b/M2/Reinforcement Learning/project/checkpoints/dqn.pkl
new file mode 100644
index 0000000..2dd831f
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diff --git a/M2/Reinforcement Learning/project/checkpoints/q_learning.pkl b/M2/Reinforcement Learning/project/checkpoints/q_learning.pkl
index d76233b..4a66503 100644
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diff --git a/M2/Reinforcement Learning/project/checkpoints/sarsa.pkl b/M2/Reinforcement Learning/project/checkpoints/sarsa.pkl
index 1bd88bd..f3fa673 100644
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diff --git a/M2/Reinforcement Learning/project/plots/DQN_training_curves.png b/M2/Reinforcement Learning/project/plots/DQN_training_curves.png
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index fa4eb22..f524c8e 100644
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diff --git a/M2/Reinforcement Learning/project/plots/SARSA_training_curves.png b/M2/Reinforcement Learning/project/plots/SARSA_training_curves.png
index 76f9d35..f80f103 100644
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diff --git a/M2/Reinforcement Learning/project/plots/championship_results.png b/M2/Reinforcement Learning/project/plots/championship_results.png
new file mode 100644
index 0000000..6008d95
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diff --git a/M2/Reinforcement Learning/project/plots/evaluation_results.png b/M2/Reinforcement Learning/project/plots/evaluation_results.png
new file mode 100644
index 0000000..10814eb
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diff --git a/M2/Time Series/TD4.qmd b/M2/Time Series/TD4.qmd
new file mode 100644
index 0000000..93b6e34
--- /dev/null
+++ b/M2/Time Series/TD4.qmd	
@@ -0,0 +1,154 @@
+# Exercise 1 : 
+
+```{r}
+set.seed(123)
+
+n <- 100
+t <- 1:n
+eps <- rnorm(n, mean = 0, sd = 1)
+
+X <- eps
+Y <- 3 * t + 2 + 15 * eps
+Z <- 3 * t + 2 + 15 * eps + 55 * sin(t * pi / 6)
+
+par(mfrow = c(3, 1))
+
+plot(ts(X), main = "Série X_t : Bruit blanc", ylab = "X_t", col = "blue")
+plot(ts(Y), main = "Série Y_t : Tendance + bruit", ylab = "Y_t", col = "red")
+plot(
+  ts(Z),
+  main = "Série Z_t : Tendance + saisonnalité + bruit",
+  ylab = "Z_t",
+  col = "darkgreen"
+)
+```
+
+```{r}
+library(forecast)
+
+alpha_vals <- c(0.1, 0.3, 0.5, 0.7, 0.9)
+
+mse_simple <- function(series) {
+  mse <- c()
+
+  for (a in alpha_vals) {
+    fit <- ses(series, alpha = a, initial = "simple", h = 1)
+
+    fitted_vals <- fitted(fit)
+    mse <- c(mse, mean((series - fitted_vals)^2, na.rm = TRUE))
+  }
+
+  data.frame(alpha = alpha_vals, MSE = mse)
+}
+
+mse_simple(X)
+mse_simple(Y)
+mse_simple(Z)
+```
+
+```{r}
+holt_mse <- function(series) {
+  beta_vals <- seq(0.1, 0.9, 0.2)
+  alpha_vals <- seq(0.1, 0.9, 0.2)
+
+  res <- expand.grid(alpha = alpha_vals, beta = beta_vals)
+  res$MSE <- NA_real_
+
+  for (i in seq_len(nrow(res))) {
+    fit <- tryCatch(
+      suppressWarnings(forecast::holt(
+        series,
+        alpha = res$alpha[i],
+        beta = res$beta[i],
+        h = 1
+      )),
+      error = function(e) NULL
+    )
+
+    if (!is.null(fit)) {
+      fitted_vals <- stats::fitted(fit)
+      res$MSE[i] <- mean((series - fitted_vals)^2, na.rm = TRUE)
+    } else {
+      res$MSE[i] <- NA_real_
+    }
+  }
+
+  res[order(res$MSE, na.last = TRUE), , drop = FALSE][1:5, ]
+}
+
+holt_mse(X)
+holt_mse(Y)
+holt_mse(Z)
+```
+
+```{r}
+fit_X <- ses(X)
+fit_Y <- ses(Y)
+fit_Z <- ses(Z)
+
+accuracy(fit_X)
+accuracy(fit_Y)
+accuracy(fit_Z)
+```
+
+```{r}
+fit_X_holt <- holt(X)
+fit_Y_holt <- holt(Y)
+fit_Z_holt <- holt(Z)
+
+accuracy(fit_X_holt)
+accuracy(fit_Y_holt)
+accuracy(fit_Z_holt)
+```
+
+# Exercise 2
+
+```{r}
+library(tseries)
+library(forecast)
+data("AirPassengers")
+log_air <- log(AirPassengers)
+
+n <- length(log_air)
+n_test <- 12
+train <- window(log_air, end = c(1959, 12))
+test <- window(log_air, start = c(1960, 1))
+
+plot(log_air, main = "Série AirPassengers (Log)")
+lines(train, col = "blue")
+lines(test, col = "red")
+legend("topleft", legend = c("Train", "Test"), col = c("blue", "red"), lty = 1)
+```
+
+```{r}
+library(forecast)
+
+fit_simple <- ses(train, h = n_test)
+fit_holt <- holt(train, h = n_test)
+
+plot(log_air)
+lines(fit_simple$mean, col = "green", lwd = 2)
+lines(fit_holt$mean, col = "orange", lwd = 2)
+legend(
+  "topleft",
+  legend = c("Série", "Simple", "Holt"),
+  col = c("black", "green", "orange"),
+  lty = 1
+)
+
+accuracy(fit_simple, test)
+accuracy(fit_holt, test)
+```
+
+```{r}
+fit_hw_add <- hw(train, h = n_test, seasonal = "additive")
+fit_hw_mult <- hw(train, h = n_test, seasonal = "multiplicative")
+
+plot(log_air)
+lines(fit_hw_add$mean, col = "purple", lwd = 2)
+lines(fit_hw_mult$mean, col = "darkgreen", lwd = 2)
+legend("topleft", legend = c("Série", "HW Additif", "HW Multiplicatif"), col = c("black", "purple", "darkgreen"), lty = 1)
+
+accuracy(fit_hw_add, test)
+accuracy(fit_hw_mult, test)
+```
diff --git a/pyproject.toml b/pyproject.toml
index 6762760..1e22747 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,51 +5,27 @@ description = "A curated collection of mathematics and data science projects dev
 readme = "README.md"
 requires-python = ">= 3.12,<3.14"
 dependencies = [
-    "accelerate>=1.12.0",
     "ale-py>=0.11.2",
     "catboost>=1.2.10",
-    "datasets>=4.6.1",
-    "faiss-cpu>=1.13.2",
-    "folium>=0.20.0",
-    "geopandas>=1.1.2",
-    "google-api-python-client>=2.191.0",
-    "google-auth-oauthlib>=1.3.0",
-    "google-generativeai>=0.8.6",
-    "gymnasium[toy-text]>=1.2.3",
-    "imblearn>=0.0",
     "ipykernel>=7.2.0",
     "ipywidgets>=8.1.8",
     "langchain>=1.2.10",
-    "langchain-community>=0.4.1",
     "langchain-core>=1.2.17",
     "langchain-huggingface>=1.2.1",
     "langchain-mistralai>=1.1.1",
-    "langchain-ollama>=1.0.1",
-    "langchain-openai>=1.1.10",
-    "langchain-text-splitters>=1.1.1",
-    "mapclassify>=2.10.0",
     "matplotlib>=3.10.8",
-    "nbformat>=5.10.4",
     "numpy>=2.4.2",
     "opencv-python>=4.13.0.92",
-    "openpyxl>=3.1.5",
     "pandas>=3.0.1",
     "pandas-stubs>=3.0.0.260204",
     "pettingzoo[atari]>=1.24.3",
     "plotly>=6.6.0",
-    "polars>=1.38.1",
-    "pypdf>=6.7.5",
-    "rasterio>=1.5.0",
-    "requests>=2.32.5",
     "scikit-learn>=1.8.0",
     "scipy>=1.17.1",
     "seaborn>=0.13.2",
-    "sentence-transformers>=5.2.3",
     # "sequenzo>=0.1.20",
     "shap>=0.50.0",
     "spacy>=3.8.11",
-    "statsmodels>=0.14.6",
-    "supersuit>=3.10.0",
     "tensorflow>=2.20.0",
     "tf-keras>=2.20.1",
     "tiktoken>=0.12.0",
@@ -57,9 +33,7 @@ dependencies = [
     "torch>=2.10.0",
     "umap-learn>=0.5.11",
     "uv>=0.10.7",
-    "wbdata>=1.1.0",
     "xgboost>=3.2.0",
-    "yfinance>=1.2.0",
 ]
 
 [dependency-groups]
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+++ b/uv.lock
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 ]
 
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