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--- |
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library_name: stable-baselines3 |
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tags: |
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- LunarLander-v2 |
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- deep-reinforcement-learning |
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- reinforcement-learning |
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- stable-baselines3 |
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model-index: |
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- name: PPO |
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results: |
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- task: |
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type: reinforcement-learning |
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name: reinforcement-learning |
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dataset: |
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name: LunarLander-v2 |
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type: LunarLander-v2 |
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metrics: |
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- type: mean_reward |
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value: 285.36 +/- 14.99 |
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name: mean_reward |
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verified: false |
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--- |
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# **PPO** Agent playing **LunarLander-v2** |
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This is a trained model of a **PPO** agent playing **LunarLander-v2** |
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). |
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## Usage (with Stable-baselines3) |
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```python |
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from huggingface_sb3 import load_from_hub |
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from stable_baselines3 import PPO |
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from stable_baselines3.common.evaluation import evaluate_policy |
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from stable_baselines3.common.env_util import make_vec_env |
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repo_id = "kinkpunk/Lunar-Landing-Program" |
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filename = "LunarProgram-PPO.zip" |
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custom_objects = { |
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"learning_rate": 0.0, |
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"lr_schedule": lambda _: 0.0, |
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"clip_range": lambda _: 0.0, |
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} |
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checkpoint = load_from_hub(repo_id, filename) |
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model = PPO.load(checkpoint, |
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custom_objects=custom_objects, |
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print_system_info=True) |
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env = make_vec_env('LunarLander-v2', n_envs=1) |
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# Evaluate the model |
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mean_reward, std_reward = evaluate_policy(model, env, |
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n_eval_episodes=10, |
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deterministic=True) |
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# Print the results |
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print('mean_reward={:.2f} +/- {:.2f}'.format(mean_reward, std_reward)) |
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``` |
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## Training (with Stable-baselines3) |
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```python |
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from huggingface_sb3 import load_from_hub |
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from stable_baselines3 import PPO |
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from stable_baselines3.common.evaluation import evaluate_policy |
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from stable_baselines3.common.env_util import make_vec_env |
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# Create the evaluation envs |
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env = make_vec_env('LunarLander-v2', n_envs=16) |
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env = gym.make('LunarLander-v2') |
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# Instantiate the agent |
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model = PPO( |
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policy = 'MlpPolicy', |
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env = env, |
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n_steps = 1024, |
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batch_size = 32, |
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n_epochs = 8, |
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gamma = 0.99, |
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gae_lambda = 0.95, |
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ent_coef = 0.01, |
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verbose=1, |
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seed=2022) |
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# Train |
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model.learn(total_timesteps=1500000) |
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# Save model |
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model_name = "Any-Name" |
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model.save(model_name) |
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``` |
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