Reinforcement Learning
stable-baselines3
English
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use prashanthgowni/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use prashanthgowni/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="prashanthgowni/ppo-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 6f329c8
Update README.md
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README.md
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value: 277.82 +/- 22.28
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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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```python
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from stable_baselines3 import
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from huggingface_sb3 import load_from_hub
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value: 277.82 +/- 22.28
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name: mean_reward
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verified: false
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language:
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- en
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---
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# **PPO** Agent playing **LunarLander-v2**
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```python
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from stable_baselines3 import PPO
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from stable_baselines3.common.env_util import make_vec_env
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from stable_baselines3.common.evaluation import evaluate_policy
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from huggingface_sb3 import load_from_hub
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# Download the model checkpoint
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model_checkpoint = load_from_hub("prashanthgowni/ppo-LunarLander-v2", "ppo-LunarLander-v2")
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# Create a vectorized environment
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env = make_vec_env("LunarLander-v2", n_envs=1)
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# Load the model
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model = PPO.load(model_checkpoint, env=env)
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# Evaluate
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print("Evaluating model")
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mean_reward, std_reward = evaluate_policy(
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model,
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env,
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n_eval_episodes=30,
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deterministic=True,
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)
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print(f"Mean reward = {mean_reward:.2f} +/- {std_reward}")
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# Start a new episode
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obs = env.reset()
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try:
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while True:
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action, state = model.predict(obs, deterministic=True)
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obs, reward, done, info = env.step(action)
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env.render()
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except KeyboardInterrupt:
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pass
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```
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