mirror of
https://github.com/ArthurDanjou/ArtStudies.git
synced 2026-01-14 15:54:13 +01:00
Refactor code for improved readability and consistency across multiple Jupyter notebooks
- Added missing commas in various print statements and function calls for better syntax. - Reformatted code to enhance clarity, including breaking long lines and aligning parameters. - Updated function signatures to use float type for sigma parameters instead of int for better precision. - Cleaned up comments and documentation strings for clarity and consistency. - Ensured consistent formatting in plotting functions and data handling.
This commit is contained in:
@@ -299,14 +299,15 @@
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"\n",
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" # With probability 1−ε: exploit (choose an arm with the highest estimated value).\n",
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" max_val = np.max(\n",
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" Q\n",
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" Q,\n",
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" ) # Compute the maximum value of the array Q and store it in the variable max_val\n",
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" candidates = np.isclose(\n",
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" Q, max_val\n",
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" Q,\n",
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" max_val,\n",
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" ) # (see Hint) Find all positions in Q where the value equals max_val.\n",
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"\n",
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" return np.random.choice(\n",
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" candidates\n",
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" candidates,\n",
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" ) # pick one of those best arms uniformly at random."
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]
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},
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@@ -614,13 +615,16 @@
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"\n",
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" # record what happens at each time step\n",
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" rewards = np.zeros(\n",
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" T, dtype=float\n",
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" T,\n",
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" dtype=float,\n",
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" ) # rewards[t] = observed reward at step t (0 or 1),\n",
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" chose_opt = np.zeros(\n",
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" T, dtype=float\n",
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" T,\n",
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" dtype=float,\n",
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" ) # chose_opt[t] = 1 if the chosen arm equals opt_arm, else 0,\n",
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" regret = np.zeros(\n",
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" T, dtype=float\n",
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" T,\n",
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" dtype=float,\n",
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" ) # regret[t]=p^*-R_t, which means how much we “missed” compared to the best arm\n",
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"\n",
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" # -------------------------------------------------\n",
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@@ -714,7 +718,11 @@
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"outputs": [],
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"source": [
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"def run_many(\n",
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" runs: int, T: int, epsilon: float, k: int, update_style: str = \"incremental\"\n",
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" runs: int,\n",
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" T: int,\n",
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" epsilon: float,\n",
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" k: int,\n",
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" update_style: str = \"incremental\",\n",
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"):\n",
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" \"\"\"Run multiple independent experiments of T time steps using ε-greedy on a k-armed Bernoulli bandit.\n",
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"\n",
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@@ -844,7 +852,9 @@
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"plt.figure(figsize=(10, 6))\n",
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"for eps in eps_list:\n",
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" plt.plot(\n",
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" results[eps][\"avg_reward\"], label=f\"ε={eps}\", color=colors[eps_list.index(eps)]\n",
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" results[eps][\"avg_reward\"],\n",
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" label=f\"ε={eps}\",\n",
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" color=colors[eps_list.index(eps)],\n",
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" )\n",
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"plt.xlabel(\"Step\")\n",
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"plt.ylabel(\"Average reward\")\n",
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@@ -856,7 +866,9 @@
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"plt.figure(figsize=(10, 6))\n",
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"for eps in eps_list:\n",
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" plt.plot(\n",
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" results[eps][\"avg_opt\"], label=f\"ε={eps}\", color=colors[eps_list.index(eps)]\n",
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" results[eps][\"avg_opt\"],\n",
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" label=f\"ε={eps}\",\n",
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" color=colors[eps_list.index(eps)],\n",
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" )\n",
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"plt.xlabel(\"Step\")\n",
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"plt.ylabel(\"P(select optimal arm)\")\n",
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@@ -881,7 +893,9 @@
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"plt.figure(figsize=(10, 6))\n",
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"for eps in eps_list:\n",
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" plt.plot(\n",
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" results[eps][\"avg_cumreg\"], label=f\"ε={eps}\", color=colors[eps_list.index(eps)]\n",
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" results[eps][\"avg_cumreg\"],\n",
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" label=f\"ε={eps}\",\n",
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" color=colors[eps_list.index(eps)],\n",
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" )\n",
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"plt.xlabel(\"Step\")\n",
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"plt.ylabel(\"Average cumulative regret\")\n",
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@@ -929,7 +943,7 @@
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"# Calculate final performance metrics for each epsilon\n",
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"print(\"### Performance Summary for Different ε Values\\n\")\n",
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"print(\n",
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" f\"{'ε':<6} {'Final Avg Reward':<18} {'Final Opt %':<15} {'Final Cum Reward':<18} {'Final Cum Regret':<18}\"\n",
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" f\"{'ε':<6} {'Final Avg Reward':<18} {'Final Opt %':<15} {'Final Cum Reward':<18} {'Final Cum Regret':<18}\",\n",
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")\n",
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"print(\"-\" * 80)\n",
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"\n",
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@@ -940,7 +954,7 @@
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" final_cum_regret = results[eps][\"avg_cumreg\"][-1]\n",
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"\n",
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" print(\n",
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" f\"{eps:<6.2f} {final_avg_reward:<18.4f} {final_opt_prob:<15.2f} {final_cum_reward:<18.2f} {final_cum_regret:<18.2f}\"\n",
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" f\"{eps:<6.2f} {final_avg_reward:<18.4f} {final_opt_prob:<15.2f} {final_cum_reward:<18.2f} {final_cum_regret:<18.2f}\",\n",
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" )\n",
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"\n",
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"# Find the best epsilon based on multiple criteria\n",
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@@ -1029,10 +1043,10 @@
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"t_incr = time_runner(\"incremental\")\n",
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"\n",
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"print(\n",
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" f\"Naive sample-mean total time over {RUNS_num} runs × {T_time} steps: {t_naive:.3f} s\"\n",
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" f\"Naive sample-mean total time over {RUNS_num} runs × {T_time} steps: {t_naive:.3f} s\",\n",
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")\n",
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"print(\n",
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" f\"Incremental sample-mean total time over {RUNS_num} runs × {T_time} steps: {t_incr:.3f} s\"\n",
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" f\"Incremental sample-mean total time over {RUNS_num} runs × {T_time} steps: {t_incr:.3f} s\",\n",
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")\n",
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"print(f\"Speedup (naive / incremental): {t_naive / t_incr:.2f}×\")"
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]
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@@ -40,11 +40,12 @@
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"import numpy as np\n",
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"\n",
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"np.set_printoptions(\n",
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" precision=3, suppress=True\n",
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" precision=3,\n",
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" suppress=True,\n",
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") # (not mandatory) This line is for limiting floats to 3 decimal places, avoiding scientific notation (like 1.23e-04) for small numbers.\n",
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"\n",
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"# For reproducibility\n",
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"rng = np.random.default_rng(seed=42) # This line creates a random number generator.\n"
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"rng = np.random.default_rng(seed=42) # This line creates a random number generator."
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]
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},
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{
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@@ -110,7 +111,7 @@
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" \"#..#..G\",\n",
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" \"#..X..#\",\n",
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" \"#######\",\n",
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"]\n"
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"]"
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]
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},
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{
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@@ -142,7 +143,7 @@
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"n_rows = len(maze_str)\n",
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"print(n_rows)\n",
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"n_cols = len(maze_str[0])\n",
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"print(n_cols)\n"
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"print(n_cols)"
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]
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},
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{
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@@ -169,7 +170,7 @@
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"source": [
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"print(\"Maze:\")\n",
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"for row in maze_str:\n",
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" print(row)\n"
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" print(row)"
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]
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},
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{
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@@ -207,7 +208,7 @@
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" \"S\",\n",
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" \"G\",\n",
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" \"X\",\n",
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"} # The vector Free represents cells that the agent is allowed to move into.\n"
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"} # The vector Free represents cells that the agent is allowed to move into."
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]
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},
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{
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@@ -277,7 +278,7 @@
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"print(\"Number of states (non-wall cells):\", n_states)\n",
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"print(\"Start state:\", start_state, \"at\", state_to_pos[start_state])\n",
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"print(\"Goal states:\", goal_states, \"at\", state_to_pos[goal_states[0]])\n",
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"print(\"Trap states:\", trap_states, \"at\", state_to_pos[trap_states[0]])\n"
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"print(\"Trap states:\", trap_states, \"at\", state_to_pos[trap_states[0]])"
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]
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},
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{
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@@ -304,7 +305,7 @@
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],
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"source": [
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"my_dict = {\"key1\": \"value1\", \"key2\": \"value2\"}\n",
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"print(my_dict[\"key2\"])\n"
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"print(my_dict[\"key2\"])"
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]
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},
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{
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@@ -383,7 +384,7 @@
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"def plot_maze_with_states():\n",
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" \"\"\"Plot the maze with state indices.\"\"\"\n",
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" grid = np.ones(\n",
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" (n_rows, n_cols)\n",
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" (n_rows, n_cols),\n",
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" ) # Start with a matrix of ones. Here 1 means “free cell”\n",
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" for i in range(n_rows):\n",
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" for j in range(n_cols):\n",
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@@ -571,7 +572,7 @@
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" # If the next cell is a wall, the robot stays in place.\n",
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" return i, j\n",
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"\n",
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" return candidate_i, candidate_j # Otherwise, return the new position\n"
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" return candidate_i, candidate_j # Otherwise, return the new position"
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]
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},
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{
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@@ -601,7 +602,7 @@
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"outputs": [],
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"source": [
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"gamma = 0.95\n",
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"p_error = 0.1 # probability of the error to a random other direction\n"
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"p_error = 0.1 # probability of the error to a random other direction"
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]
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},
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{
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@@ -662,7 +663,7 @@
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"# Set rewards for each state\n",
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"step_penalty = -0.01\n",
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"goal_reward = 1.0\n",
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"trap_reward = -1.0\n"
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"trap_reward = -1.0"
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]
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},
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{
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@@ -710,7 +711,7 @@
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" elif s in trap_states:\n",
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" R[s] = trap_reward\n",
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" else:\n",
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" R[s] = step_penalty\n"
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" R[s] = step_penalty"
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]
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},
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{
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@@ -735,7 +736,7 @@
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"\n",
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"def is_terminal(s: int) -> bool:\n",
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" \"\"\"Check if a state is terminal (goal or trap).\"\"\"\n",
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" return s in terminal_states\n"
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" return s in terminal_states"
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]
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},
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{
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@@ -797,9 +798,9 @@
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" error_i, error_j = move_deterministic(i, j, a2)\n",
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" s_error = pos_to_state[(error_i, error_j)] # get its state index s_error\n",
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" P[a, s, s_error] += p_error / len(\n",
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" other_actions\n",
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" other_actions,\n",
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" ) # add p_error / 3 to P[a, s, s_error]\n",
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"# So for each (s,a), probabilities over all s_next sum to 1.\n"
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"# So for each (s,a), probabilities over all s_next sum to 1."
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]
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},
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{
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@@ -843,7 +844,7 @@
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" # If everything is correct, they should be very close to 1.\n",
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"\n",
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" probs = P[a].sum(axis=1)\n",
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" print(f\"Action {action_names[a]}:\", probs)\n"
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" print(f\"Action {action_names[a]}:\", probs)"
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]
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},
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{
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@@ -995,7 +996,7 @@
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"\n",
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" for _it in range(max_iter): # Main iterative loop\n",
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" V_new = np.zeros_like(\n",
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" V\n",
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" V,\n",
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" ) # Create a new value vector and we will compute an updated value for each state.\n",
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"\n",
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" # Now we update each state using the Bellman expectation equation\n",
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@@ -1004,7 +1005,7 @@
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" V_new[s] = R[s] + gamma * np.sum(P[a, s, :] * V)\n",
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"\n",
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" delta = np.max(\n",
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" np.abs(V_new - V)\n",
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" np.abs(V_new - V),\n",
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" ) # This measures how much the value function changed in this iteration:\n",
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" # If delta is small, the values start to converge; otherwise, we need to keep iterating.\n",
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" V = V_new # Update V, i.e. Set the new values for the next iteration.\n",
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@@ -1012,7 +1013,7 @@
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" if delta < theta: # Check convergence: When changes are tiny, we stop.\n",
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" break\n",
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"\n",
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" return V # Return the final value function, this is our estimate for V^{pi}(s), s in the state set.\n"
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" return V # Return the final value function, this is our estimate for V^{pi}(s), s in the state set."
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]
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},
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{
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@@ -1082,7 +1083,7 @@
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"source": [
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"V_random = policy_evaluation(policy=random_policy, P=P, R=R, gamma=gamma)\n",
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"print(\"Value function under random policy:\")\n",
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"print(V_random)\n"
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"print(V_random)"
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]
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},
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{
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@@ -1127,7 +1128,8 @@
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"def plot_values(V: np.ndarray, title=\"Value function\") -> None:\n",
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" \"\"\"Plot the value function V on the maze as a heatmap.\"\"\"\n",
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" grid_values = np.full(\n",
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" (n_rows, n_cols), np.nan\n",
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" (n_rows, n_cols),\n",
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" np.nan,\n",
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" ) # Initializes a grid the same size as the maze. Every cell starts as NaN.\n",
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" for (\n",
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" s,\n",
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@@ -1152,7 +1154,13 @@
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"\n",
|
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" for s, (i, j) in state_to_pos.items():\n",
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" ax.text(\n",
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" j, i, f\"{V[s]:.2f}\", ha=\"center\", va=\"center\", color=\"white\", fontsize=9\n",
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" j,\n",
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" i,\n",
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" f\"{V[s]:.2f}\",\n",
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" ha=\"center\",\n",
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" va=\"center\",\n",
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" color=\"white\",\n",
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" fontsize=9,\n",
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" )\n",
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"\n",
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" # Remove axis ticks and set title\n",
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@@ -1162,7 +1170,7 @@
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" plt.show()\n",
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"\n",
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"\n",
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"plot_values(V_random, title=\"Value function: random policy\")\n"
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"plot_values(V_random, title=\"Value function: random policy\")"
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]
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},
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{
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@@ -1247,7 +1255,7 @@
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" ax.set_yticklabels([])\n",
|
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" ax.grid(True)\n",
|
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" ax.set_title(title)\n",
|
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" plt.show()\n"
|
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" plt.show()"
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]
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},
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{
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@@ -1276,7 +1284,7 @@
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}
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],
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"source": [
|
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"plot_policy(policy=random_policy, title=\"Policy\")\n"
|
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"plot_policy(policy=random_policy, title=\"Policy\")"
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]
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},
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{
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@@ -23,11 +23,12 @@
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"import numpy as np\n",
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"\n",
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"np.set_printoptions(\n",
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" precision=3, suppress=True\n",
|
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" precision=3,\n",
|
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" suppress=True,\n",
|
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") # (not mandatory) This line is for limiting floats to 3 decimal places, avoiding scientific notation (like 1.23e-04) for small numbers.\n",
|
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"\n",
|
||||
"# For reproducibility\n",
|
||||
"rng = np.random.default_rng(seed=42) # This line creates a random number generator.\n"
|
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"rng = np.random.default_rng(seed=42) # This line creates a random number generator."
|
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]
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},
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{
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@@ -102,7 +103,7 @@
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" \"S\",\n",
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" \"G\",\n",
|
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" \"X\",\n",
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"} # The vector Free represents cells that the agent is allowed to move into.\n"
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"} # The vector Free represents cells that the agent is allowed to move into."
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]
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},
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{
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@@ -164,7 +165,7 @@
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"print(\"Number of states (non-wall cells):\", n_states)\n",
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"print(\"Start state:\", start_state, \"at\", state_to_pos[start_state])\n",
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"print(\"Goal states:\", goal_states, \"at\", state_to_pos[goal_states[0]])\n",
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"print(\"Trap states:\", trap_states, \"at\", state_to_pos[trap_states[0]])\n"
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"print(\"Trap states:\", trap_states, \"at\", state_to_pos[trap_states[0]])"
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]
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},
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{
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@@ -188,7 +189,7 @@
|
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"def plot_maze_with_states():\n",
|
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" \"\"\"Plot the maze with state indices.\"\"\"\n",
|
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" grid = np.ones(\n",
|
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" (n_rows, n_cols)\n",
|
||||
" (n_rows, n_cols),\n",
|
||||
" ) # Start with a matrix of ones. Here 1 means “free cell”\n",
|
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" for i in range(n_rows):\n",
|
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" for j in range(n_cols):\n",
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@@ -316,7 +317,7 @@
|
||||
" # If the next cell is a wall, the robot stays in place.\n",
|
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" return i, j\n",
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"\n",
|
||||
" return candidate_i, candidate_j # Otherwise, return the new position\n"
|
||||
" return candidate_i, candidate_j # Otherwise, return the new position"
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||||
]
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},
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{
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@@ -335,7 +336,7 @@
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||||
"outputs": [],
|
||||
"source": [
|
||||
"gamma = 0.95\n",
|
||||
"p_error = 0.1 # probability of the error to a random other direction\n"
|
||||
"p_error = 0.1 # probability of the error to a random other direction"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -360,7 +361,7 @@
|
||||
"# Set rewards for each state\n",
|
||||
"step_penalty = -0.01\n",
|
||||
"goal_reward = 1.0\n",
|
||||
"trap_reward = -1.0\n"
|
||||
"trap_reward = -1.0"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -376,7 +377,7 @@
|
||||
" elif s in trap_states:\n",
|
||||
" R[s] = trap_reward\n",
|
||||
" else:\n",
|
||||
" R[s] = step_penalty\n"
|
||||
" R[s] = step_penalty"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -391,7 +392,7 @@
|
||||
"\n",
|
||||
"def is_terminal(s: int) -> bool:\n",
|
||||
" \"\"\"Check if a state is terminal (goal or trap).\"\"\"\n",
|
||||
" return s in terminal_states\n"
|
||||
" return s in terminal_states"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -437,9 +438,9 @@
|
||||
" error_i, error_j = move_deterministic(i, j, a2)\n",
|
||||
" s_error = pos_to_state[(error_i, error_j)] # get its state index s_error\n",
|
||||
" P[a, s, s_error] += p_error / len(\n",
|
||||
" other_actions\n",
|
||||
" other_actions,\n",
|
||||
" ) # add p_error / 3 to P[a, s, s_error]\n",
|
||||
"# So for each (s,a), probabilities over all s_next sum to 1.\n"
|
||||
"# So for each (s,a), probabilities over all s_next sum to 1."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -476,7 +477,7 @@
|
||||
" # If everything is correct, they should be very close to 1.\n",
|
||||
"\n",
|
||||
" probs = P[a].sum(axis=1)\n",
|
||||
" print(f\"Action {action_names[a]}:\", probs)\n"
|
||||
" print(f\"Action {action_names[a]}:\", probs)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -520,7 +521,7 @@
|
||||
"\n",
|
||||
" for _it in range(max_iter): # Main iterative loop\n",
|
||||
" V_new = np.zeros_like(\n",
|
||||
" V\n",
|
||||
" V,\n",
|
||||
" ) # Create a new value vector and we will compute an updated value for each state.\n",
|
||||
"\n",
|
||||
" # Now we update each state using the Bellman expectation equation\n",
|
||||
@@ -529,7 +530,7 @@
|
||||
" V_new[s] = R[s] + gamma * np.sum(P[a, s, :] * V)\n",
|
||||
"\n",
|
||||
" delta = np.max(\n",
|
||||
" np.abs(V_new - V)\n",
|
||||
" np.abs(V_new - V),\n",
|
||||
" ) # This measures how much the value function changed in this iteration:\n",
|
||||
" # If delta is small, the values start to converge; otherwise, we need to keep iterating.\n",
|
||||
" V = V_new # Update V, i.e. Set the new values for the next iteration.\n",
|
||||
@@ -537,7 +538,7 @@
|
||||
" if delta < theta: # Check convergence: When changes are tiny, we stop.\n",
|
||||
" break\n",
|
||||
"\n",
|
||||
" return V # Return the final value function, this is our estimate for V^{pi}(s), s in the state set.\n"
|
||||
" return V # Return the final value function, this is our estimate for V^{pi}(s), s in the state set."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -550,7 +551,8 @@
|
||||
"def plot_values(V: np.ndarray, title=\"Value function\") -> None:\n",
|
||||
" \"\"\"Plot the value function V on the maze as a heatmap.\"\"\"\n",
|
||||
" grid_values = np.full(\n",
|
||||
" (n_rows, n_cols), np.nan\n",
|
||||
" (n_rows, n_cols),\n",
|
||||
" np.nan,\n",
|
||||
" ) # Initializes a grid the same size as the maze. Every cell starts as NaN.\n",
|
||||
" for (\n",
|
||||
" s,\n",
|
||||
@@ -575,14 +577,20 @@
|
||||
"\n",
|
||||
" for s, (i, j) in state_to_pos.items():\n",
|
||||
" ax.text(\n",
|
||||
" j, i, f\"{V[s]:.2f}\", ha=\"center\", va=\"center\", color=\"white\", fontsize=9\n",
|
||||
" j,\n",
|
||||
" i,\n",
|
||||
" f\"{V[s]:.2f}\",\n",
|
||||
" ha=\"center\",\n",
|
||||
" va=\"center\",\n",
|
||||
" color=\"white\",\n",
|
||||
" fontsize=9,\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" # Remove axis ticks and set title\n",
|
||||
" ax.set_xticks([])\n",
|
||||
" ax.set_yticks([])\n",
|
||||
" ax.set_title(title)\n",
|
||||
" plt.show()\n"
|
||||
" plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -659,7 +667,7 @@
|
||||
" ax.set_yticklabels([])\n",
|
||||
" ax.grid(True)\n",
|
||||
" ax.set_title(title)\n",
|
||||
" plt.show()\n"
|
||||
" plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -716,7 +724,7 @@
|
||||
"source": [
|
||||
"V_random = policy_evaluation(policy=random_policy, P=P, R=R, gamma=gamma)\n",
|
||||
"print(\"Value function under random policy:\")\n",
|
||||
"print(V_random)\n"
|
||||
"print(V_random)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -748,7 +756,7 @@
|
||||
],
|
||||
"source": [
|
||||
"plot_values(V_random, title=\"Value function: random policy\")\n",
|
||||
"plot_policy(policy=random_policy, title=\"Random Policy\")\n"
|
||||
"plot_policy(policy=random_policy, title=\"Random Policy\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -847,7 +855,7 @@
|
||||
"V_my_policy = policy_evaluation(policy=my_policy, P=P, R=R, gamma=gamma)\n",
|
||||
"\n",
|
||||
"plot_values(V=V_my_policy, title=\"Value function: my policy\")\n",
|
||||
"plot_policy(policy=my_policy, title=\"My policy\")\n"
|
||||
"plot_policy(policy=my_policy, title=\"My policy\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
Reference in New Issue
Block a user