README.md

metadata

library_name: stable-baselines3
tags:
  - LunarLander-v2
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
model-index:
  - name: PPO
    results:
      - task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: LunarLander-v2
          type: LunarLander-v2
        metrics:
          - type: mean_reward
            value: 249.81 +/- 23.73
            name: mean_reward
            verified: false

PPO Agent playing LunarLander-v2

This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.

Usage (with Stable-baselines3)

from stable_baselines3 import PPO
from stable_baselines3.common.envs import LunarLander
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.evaluation import evaluate_policy

# Create the LunarLander environment
env = LunarLander()

# Vectorize the environment for parallel training
vec_env = make_vec_env('LunarLander-v2', n_envs=16)

# Instantiate the PPO agent
model = PPO("MlpPolicy", vec_env, verbose=1)

# Train the agent
model.learn(total_timesteps=int(2e5))

# Evaluate the trained agent
eval_env = LunarLander()
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

# Save the trained model
model_name = "ppo-LunarLander-v2"
model.save(model_name)

# Package and upload the model to the Hub
from huggingface_sb3 import package_to_hub
package_to_hub(model=model,
               model_name=model_name,
               model_architecture="PPO",
               env_id="LunarLander-v2",
               eval_env=eval_env,
               repo_id="your-username/ppo-LunarLander-v2",
               commit_message="Upload PPO LunarLander-v2 trained agent")

Ensure to replace "your-username" with your Hugging Face username.