From 88f45deba9b6025f2ff66d69e7e5e9db36a73a06 Mon Sep 17 00:00:00 2001
From: Arthur DANJOU <arthurdanjou@outlook.fr>
Date: Thu, 15 Jan 2026 11:50:46 +0100
Subject: [PATCH] Update execution counts in Jupyter notebooks and replace
 CliffWalking GIF with a new version

---
 M2/Reinforcement Learning/CliffWalking.gif    |   Bin 94272 -> 89039 bytes
 ... 5A - TD prediction with random walk.ipynb |    30 +-
 ...ab 5B - TD control with CliffWalking.ipynb | 15418 ++--------------
 3 files changed, 1052 insertions(+), 14396 deletions(-)

diff --git a/M2/Reinforcement Learning/CliffWalking.gif b/M2/Reinforcement Learning/CliffWalking.gif
index a4c7f3be9f1319d2b384910cff64f7977eee74f6..d4c22c46454603faba906a593694582784ba6bf5 100644
GIT binary patch
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diff --git a/M2/Reinforcement Learning/Lab 5A - TD prediction with random walk.ipynb b/M2/Reinforcement Learning/Lab 5A - TD prediction with random walk.ipynb
index defc029..22e34c6 100644
--- a/M2/Reinforcement Learning/Lab 5A - TD prediction with random walk.ipynb	
+++ b/M2/Reinforcement Learning/Lab 5A - TD prediction with random walk.ipynb	
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 1,
    "id": "55bc0488-c0e4-40f3-86d0-f77947023fff",
    "metadata": {},
    "outputs": [],
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "id": "7abd9d0e-75db-4d0c-a880-573e4cccff58",
    "metadata": {},
    "outputs": [],
@@ -215,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 3,
    "id": "f5167cca-767b-4cec-bd93-092d57b5d9ca",
    "metadata": {},
    "outputs": [
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 4,
    "id": "42f40fd2-a33d-4fda-b006-45bf3d7970e3",
    "metadata": {},
    "outputs": [],
@@ -265,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
    "id": "c33519e2-21ec-48e1-85be-e6e9c6ff33f4",
    "metadata": {},
    "outputs": [
@@ -306,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 6,
    "id": "748fb97c-caa4-4c67-bb84-b128c716eee0",
    "metadata": {},
    "outputs": [],
@@ -418,7 +418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 7,
    "id": "eded21db-775d-4182-9794-01970f1ee53f",
    "metadata": {},
    "outputs": [],
@@ -556,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "925fdbbd-3e5b-43a6-906c-b58d0e66014c",
    "metadata": {},
    "outputs": [],
@@ -639,7 +639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 9,
    "id": "527b5624-c177-4540-8081-65e567352146",
    "metadata": {},
    "outputs": [
@@ -725,7 +725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 10,
    "id": "f1018e57-bb15-4993-9af8-bab3c42b31ce",
    "metadata": {},
    "outputs": [
@@ -857,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 11,
    "id": "f3b203df-6661-4b1b-ad5a-d6743ffbb3d0",
    "metadata": {},
    "outputs": [],
@@ -946,7 +946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 12,
    "id": "89b067ed-f28d-464e-8b28-77c8370c7b0b",
    "metadata": {},
    "outputs": [
@@ -1116,7 +1116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "015472b2-21f8-40da-be30-badefa7b75c9",
    "metadata": {},
    "outputs": [],
@@ -1191,7 +1191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 14,
    "id": "e2e0020a-d65b-4c8e-ad80-adac05654d43",
    "metadata": {},
    "outputs": [
@@ -1285,7 +1285,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "studies",
+   "display_name": ".venv (3.13.9)",
    "language": "python",
    "name": "python3"
   },
diff --git a/M2/Reinforcement Learning/Lab 5B - TD control with CliffWalking.ipynb b/M2/Reinforcement Learning/Lab 5B - TD control with CliffWalking.ipynb
index 4575051..0213831 100644
--- a/M2/Reinforcement Learning/Lab 5B - TD control with CliffWalking.ipynb	
+++ b/M2/Reinforcement Learning/Lab 5B - TD control with CliffWalking.ipynb	
@@ -34,19 +34,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": null,
    "id": "439f82ff-5f9d-479f-a3dc-bd78dca39649",
    "metadata": {},
    "outputs": [],
    "source": [
+    "from pathlib import Path\n",
+    "\n",
     "import gymnasium as gym\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from collections import defaultdict\n",
-    "from PIL import Image as PIL_Image\n",
-    "from IPython.display import Image\n",
     "from gymnasium.wrappers import TimeLimit\n",
-    "from pathlib import Path"
+    "from IPython.display import Image\n",
+    "from PIL import Image as PIL_Image\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n"
    ]
   },
   {
@@ -133,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 2,
    "id": "74806c48-8830-43e5-bfc3-d258d75cacfe",
    "metadata": {
     "colab": {
@@ -170,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 3,
    "id": "344b7466-b990-4ce6-aed9-cadb10ae817b",
    "metadata": {},
    "outputs": [
@@ -203,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 4,
    "id": "0f491464-f17c-4c61-938d-f131831d9d88",
    "metadata": {},
    "outputs": [
@@ -211,7 +212,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3\n"
+      "2\n"
      ]
     }
    ],
@@ -230,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 5,
    "id": "7e9dad8c-7707-4251-8eb7-79fbae914b9e",
    "metadata": {},
    "outputs": [
@@ -261,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 6,
    "id": "a267ed76-1cfe-4c67-abd1-d55508b7c291",
    "metadata": {},
    "outputs": [
@@ -298,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 7,
    "id": "ce68e25c-21ff-4e03-a388-51cf2752d782",
    "metadata": {},
    "outputs": [
@@ -306,7 +307,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "the action is 2\n",
+      "the action is 3\n",
       "36 -1 False False\n"
      ]
     }
@@ -336,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 8,
    "id": "7a704921-8b7c-4dda-950c-e8254435c7f6",
    "metadata": {},
    "outputs": [
@@ -346,14527 +347,1153 @@
      "text": [
       "36\n",
       "24 -1 False False\n",
-      "36 -1 False False\n",
-      "24 -1 False False\n",
-      "36 -1 False False\n",
+      "25 -1 False False\n",
+      "36 -100 False False\n",
       "24 -1 False False\n",
       "12 -1 False False\n",
-      "24 -1 False False\n",
-      "36 -1 False False\n",
-      "24 -1 False False\n",
-      "24 -1 False False\n",
-      "25 -1 False False\n",
-      "13 -1 False False\n",
+      "12 -1 False False\n",
+      "0 -1 False False\n",
       "1 -1 False False\n",
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-      "14 -1 False False\n",
-      "2 -1 False False\n",
       "3 -1 False False\n",
-      "15 -1 False False\n",
-      "27 -1 False False\n",
+      "3 -1 False False\n",
       "15 -1 False False\n",
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-      "3 -1 False False\n",
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-      "25 -1 False False\n",
-      "26 -1 False False\n",
-      "27 -1 False False\n",
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-      "36 -1 False False\n",
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-      "36 -100 False False\n",
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-      "24 -1 False False\n",
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-      "24 -1 False False\n",
-      "24 -1 False False\n",
-      "36 -1 False False\n",
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-      "36 -1 False False\n",
-      "36 -1 False False\n",
-      "24 -1 False False\n",
-      "12 -1 False False\n",
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-      "14 -1 False False\n",
-      "26 -1 False False\n",
-      "25 -1 False False\n",
-      "13 -1 False False\n",
-      "14 -1 False False\n",
-      "26 -1 False False\n",
-      "27 -1 False False\n",
-      "15 -1 False False\n",
-      "14 -1 False False\n",
-      "26 -1 False False\n",
-      "25 -1 False False\n",
-      "24 -1 False False\n",
-      "25 -1 False False\n",
-      "24 -1 False False\n",
-      "24 -1 False False\n",
-      "36 -1 False False\n",
-      "36 -1 False False\n",
-      "36 -1 False False\n",
-      "36 -1 False False\n",
-      "36 -1 False False\n",
-      "24 -1 False False\n",
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-      "13 -1 False False\n",
-      "14 -1 False False\n",
-      "26 -1 False False\n",
-      "27 -1 False False\n",
-      "28 -1 False False\n",
-      "16 -1 False False\n",
-      "17 -1 False False\n",
-      "29 -1 False False\n",
-      "28 -1 False False\n",
-      "29 -1 False False\n",
-      "17 -1 False False\n",
-      "29 -1 False False\n",
-      "36 -100 False False\n",
-      "36 -1 False False\n",
-      "36 -1 False False\n",
-      "36 -1 False False\n",
-      "36 -1 False False\n",
-      "36 -1 False False\n",
-      "24 -1 False False\n",
-      "12 -1 False False\n",
-      "0 -1 False False\n",
-      "1 -1 False False\n",
-      "2 -1 False False\n",
-      "3 -1 False False\n",
-      "4 -1 False False\n",
-      "4 -1 False False\n",
-      "16 -1 False False\n",
-      "4 -1 False False\n",
-      "4 -1 False False\n",
-      "4 -1 False False\n",
-      "4 -1 False False\n",
-      "5 -1 False False\n",
-      "4 -1 False False\n",
-      "16 -1 False False\n",
-      "28 -1 False False\n",
-      "29 -1 False False\n",
-      "17 -1 False False\n",
-      "18 -1 False False\n",
-      "17 -1 False False\n",
-      "18 -1 False False\n",
-      "30 -1 False False\n",
-      "29 -1 False False\n",
-      "17 -1 False False\n",
-      "29 -1 False False\n",
-      "30 -1 False False\n",
-      "31 -1 False False\n",
-      "19 -1 False False\n",
-      "7 -1 False False\n",
-      "19 -1 False False\n",
-      "18 -1 False False\n",
-      "30 -1 False False\n",
-      "31 -1 False False\n",
-      "19 -1 False False\n",
-      "20 -1 False False\n",
-      "8 -1 False False\n",
-      "20 -1 False False\n",
-      "19 -1 False False\n",
-      "20 -1 False False\n",
-      "21 -1 False False\n",
-      "20 -1 False False\n",
-      "8 -1 False False\n",
-      "8 -1 False False\n",
-      "9 -1 False False\n",
-      "10 -1 False False\n",
-      "11 -1 False False\n",
-      "11 -1 False False\n",
-      "10 -1 False False\n",
-      "10 -1 False False\n",
-      "11 -1 False False\n",
-      "10 -1 False False\n",
-      "11 -1 False False\n",
-      "23 -1 False False\n",
-      "23 -1 False False\n",
-      "22 -1 False False\n",
-      "23 -1 False False\n",
-      "11 -1 False False\n",
-      "11 -1 False False\n",
-      "23 -1 False False\n",
-      "23 -1 False False\n",
-      "35 -1 False False\n",
       "35 -1 False False\n",
       "35 -1 False False\n",
       "47 -1 True False\n"
@@ -14899,7 +1526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": null,
    "id": "30148883-ed0e-4267-994b-0390d80de498",
    "metadata": {},
    "outputs": [
@@ -14917,9 +1544,9 @@
    "source": [
     "# What `or` means\n",
     "\n",
-    "print(\"1\", True or True)\n",
-    "print(\"2\", True or False)\n",
-    "print(\"3\", False or True)\n",
+    "print(\"1\", True or True)  # noqa: SIM222\n",
+    "print(\"2\", True or False)  # noqa: SIM222\n",
+    "print(\"3\", False or True)  # noqa: SIM222\n",
     "print(\"4\", False or False)"
    ]
   },
@@ -14935,7 +1562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 10,
    "id": "a165c6ab-0575-4ce7-a13b-a58413474519",
    "metadata": {},
    "outputs": [
@@ -14965,7 +1592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 11,
    "id": "6f8d797d-78da-4ea8-9bc2-a9de82d85474",
    "metadata": {},
    "outputs": [
@@ -15014,10 +1641,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 12,
    "id": "5e9deb59-95f1-4361-899f-6eb21d30caae",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/pkgdata.py:25: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
+      "  from pkg_resources import resource_stream, resource_exists\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -15043,7 +1678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 13,
    "id": "883bfe16-4733-4e7c-b494-2cac7c3e5ebf",
    "metadata": {},
    "outputs": [],
@@ -15078,7 +1713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 14,
    "id": "8e07b1ce-659f-4b45-9f30-58e932a9b907",
    "metadata": {},
    "outputs": [],
@@ -15127,13 +1762,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 15,
    "id": "c51792e4-2ef9-4cc2-af8f-76baec1d479f",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -15238,7 +1873,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 16,
    "id": "a348baca-a799-435a-9c20-d4f293af2193",
    "metadata": {},
    "outputs": [
@@ -15289,7 +1924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 17,
    "id": "97765e54",
    "metadata": {},
    "outputs": [],
@@ -15343,12 +1978,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": null,
    "id": "09a158f2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def train_sarsa(\n",
+    "def train_sarsa(  # noqa: PLR0913\n",
     "    env: gym.Env,\n",
     "    num_episodes: int = 5000,\n",
     "    alpha: float = 0.5,\n",
@@ -15409,7 +2044,7 @@
     "\n",
     "            # Detect cliff falls\n",
     "            if (\n",
-    "                r <= -100\n",
+    "                r <= -100  # noqa: PLR2004\n",
     "            ):  # In CliffWalking, falling off the cliff typically gives reward -100.\n",
     "                fell = 1  # if reward is very negative (≤ -100), we mark fell = 1.\n",
     "            a2 = epsilon_greedy(\n",
@@ -15451,7 +2086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 19,
    "id": "3fdbca74-0eee-4318-a2ae-952253fa8900",
    "metadata": {},
    "outputs": [],
@@ -15484,7 +2119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 20,
    "id": "6791ec0c-17b5-45c4-92d5-bc587c03afaf",
    "metadata": {},
    "outputs": [
@@ -15539,7 +2174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 21,
    "id": "d987f3d1-ffc5-4b16-80ac-5fbeb1f68b72",
    "metadata": {},
    "outputs": [
@@ -15598,7 +2233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 22,
    "id": "f045dc72-d99c-4f9d-b532-bc163f810a20",
    "metadata": {},
    "outputs": [
@@ -15685,12 +2320,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": null,
    "id": "20253859-fedd-4ee0-a4df-e7708662586e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def train_q_learning(\n",
+    "def train_q_learning( # noqa: PLR0913\n",
     "    env: gym.Env,\n",
     "    num_episodes: int = 5000,\n",
     "    alpha: float = 0.5,\n",
@@ -15698,7 +2333,24 @@
     "    epsilon: float = 0.1,\n",
     "    seed: int = 0,\n",
     ") -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n",
-    "    # TO DO\n",
+    "    \"\"\"Compute the Q-learning action-value function Q(s,a) using the Q-learning update rule.\n",
+    "\n",
+    "    Args:\n",
+    "        env (gym.Env): The environment to train on\n",
+    "        num_episodes (int): Number of episodes to train\n",
+    "        alpha (float): Learning rate\n",
+    "        gamma (float): Discount factor\n",
+    "        epsilon (float): Exploration probability for epsilon-greedy policy\n",
+    "        seed (int): Random seed for reproducibility\n",
+    "\n",
+    "    Returns:\n",
+    "        tuple: A tuple containing:\n",
+    "            - Q (np.ndarray): The learned action-value function\n",
+    "            - returns (np.ndarray): Total rewards per episode\n",
+    "            - steps_list (np.ndarray): Number of steps per episode\n",
+    "            - cliff_falls (np.ndarray): Indicator of cliff falls per episode\n",
+    "\n",
+    "    \"\"\"\n",
     "    # Compared to SARSA, the main changes are:\n",
     "    # 1) TD target uses max_a' Q(s', a') (off-policy) instead of Q(s', a_next).\n",
     "    # 2) Transition only store the state s not the state-action (s,a) pair.\n",
@@ -15740,7 +2392,7 @@
     "\n",
     "            # Detect cliff falls\n",
     "            if (\n",
-    "                r <= -100\n",
+    "                r <= -100 # noqa: PLR2004\n",
     "            ):  # In CliffWalking, falling off the cliff typically gives reward -100.\n",
     "                fell = 1  # if reward is very negative (≤ -100), we mark fell = 1.\n",
     "\n",
@@ -15770,7 +2422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "56ec0709-8678-430e-9a05-5685f6f221bc",
    "metadata": {
     "colab": {
@@ -15865,7 +2517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "76148918-69a0-4708-8f20-5b9eccad3a76",
    "metadata": {
     "id": "76148918-69a0-4708-8f20-5b9eccad3a76"
@@ -15928,7 +2580,7 @@
     "    ax.set_ylim(n_rows - 0.5, -0.5)\n",
     "    ax.set_xticks(range(n_cols))\n",
     "    ax.set_yticks(range(n_rows))\n",
-    "    ax.grid(True)\n",
+    "    ax.grid(True)  # noqa: FBT003\n",
     "    ax.set_aspect(\"equal\")\n",
     "\n",
     "    # mark cliff\n",
@@ -15988,7 +2640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "e2392a16-9bcf-4b74-bf93-40d3a351d8cc",
    "metadata": {
     "colab": {
@@ -16049,7 +2701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 28,
    "id": "3712fe5e-86a6-40db-9054-8f4a2ea6db86",
    "metadata": {},
    "outputs": [
@@ -16103,7 +2755,10 @@
    "outputs": [],
    "source": [
     "def rollout_policy(\n",
-    "    env: gym.Env, pi: np.ndarray, max_steps: int = 200, seed: int = 0,\n",
+    "    env: gym.Env,\n",
+    "    pi: np.ndarray,\n",
+    "    max_steps: int = 200,\n",
+    "    seed: int = 0,\n",
     ") -> tuple[list[int], list[float], bool, bool]:\n",
     "    \"\"\"Rollout a policy pi in the environment env for at most max_steps.\n",
     "\n",
@@ -16211,7 +2866,8 @@
     "\n",
     "states, rewards, term, trunc = rollout_policy(env, pi_sarsa, seed=123)\n",
     "plot_trajectory(\n",
-    "    states, title=f\"SARSA greedy rollout | return={sum(rewards)} | steps={len(rewards)}\",\n",
+    "    states,\n",
+    "    title=f\"SARSA greedy rollout | return={sum(rewards)} | steps={len(rewards)}\",\n",
     ")\n",
     "\n",
     "states, rewards, term, trunc = rollout_policy(env, pi_ql, seed=123)\n",
@@ -16280,7 +2936,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "studies",
+   "display_name": ".venv (3.13.9)",
    "language": "python",
    "name": "python3"
   },