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import os |
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import random |
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from hf_helpers.sb3_eval import eval_model_with_seed |
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import pandas as pd |
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env_id = "LunarLander-v2" |
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models_to_evaluate = [ |
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"ppo-LunarLander-v2_001_000_000_hf_defaults.zip", |
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"ppo-LunarLander-v2_010_000_000_hf_defaults.zip", |
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"ppo-LunarLander-v2_010_000_000_sb3_defaults.zip", |
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"ppo-LunarLander-v2_123_456_789_hf_defaults.zip", |
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] |
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evaluation_results_fp = "evaluation_results.csv" |
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def store_results(results): |
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results_df = pd.DataFrame(results) |
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header = False if os.path.exists(evaluation_results_fp) else True |
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results_df.to_csv(evaluation_results_fp, mode="a", index=False, header=header) |
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def evaluate_and_store_all_results(): |
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results = [] |
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n_evaluations = 1000 |
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for i in range(n_evaluations): |
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if i > 0 and i % 10 == 0: |
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print(f"Progress: {i}/{n_evaluations}") |
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store_results(results) |
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results = [] |
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seed = random.randint(0, 10000) |
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n_envs = random.randint(1, 16) |
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for model_fp in models_to_evaluate: |
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result, mean_reward, std_reward = eval_model_with_seed( |
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model_fp, env_id, seed, n_eval_episodes=10, n_envs=n_envs |
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) |
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result_data = { |
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"model_fp": model_fp, |
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"seed": seed, |
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"n_envs": n_envs, |
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"result": result, |
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"mean_reward": mean_reward, |
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"std_reward": std_reward, |
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} |
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results.append(result_data) |
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def analyze_results(): |
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results_df = pd.read_csv(evaluation_results_fp) |
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results_df["model_fp"] = results_df["model_fp"].str.replace(".zip", "", regex=False) |
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aggregated_results = ( |
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results_df.groupby("model_fp")["result"] |
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.agg(["count", "min", "max", "mean"]) |
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.reset_index() |
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) |
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aggregated_results.columns = [ |
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"Model name", |
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"Number of results", |
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"Min", |
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"Max", |
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"Average", |
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] |
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aggregated_results = aggregated_results.sort_values(by="Model name") |
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print(aggregated_results.to_markdown(index=False, tablefmt="pipe")) |
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analyze_results() |
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