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: 242.40 +/- 15.91
            name: mean_reward
            verified: false

PPO Agent playing LunarLander-v2

This model is trained using PPO [proximal policy optimization algorithm invented by OpenAI] The RL-based agent playing to land correctly on the moon using LunarLander environment as simulator.

Usage (with Stable-baselines3)

TODO: Add your code

from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub

repo_id = "innocent-charles/RL-ppo-LunarLander-v2"
filename = "RL-ppo-LunarLander-v2.zip"

custom_objects = {
            "learning_rate": 0.0,
            "lr_schedule": lambda _: 0.0,
            "clip_range": lambda _: 0.0,
}

checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True)

...