README.md

metadata

library_name: stable-baselines3
tags:
  - LunarLander-v2
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
model-index:
  - name: >-
      PPO( policy = 'MlpPolicy', env = env, n_steps = 1024, batch_size = 64,
      n_epochs = 4, gamma = 0.999, gae_lambda = 0.98, ent_coef = 0.01,
      verbose=1)
    results:
      - task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: LunarLander-v2
          type: LunarLander-v2
        metrics:
          - type: mean_reward
            value: 242.46 +/- 18.20
            name: mean_reward
            verified: false

PPO( policy = 'MlpPolicy', env = env, n_steps = 1024, batch_size = 64, n_epochs = 4, gamma = 0.999, gae_lambda = 0.98, ent_coef = 0.01, verbose=1) Agent playing LunarLander-v2

This is a trained model of a PPO( policy = 'MlpPolicy', env = env, n_steps = 1024, batch_size = 64, n_epochs = 4, gamma = 0.999, gae_lambda = 0.98, ent_coef = 0.01, verbose=1) agent playing LunarLander-v2 using the stable-baselines3 library.

Usage (with Stable-baselines3)

TODO: Add your code

from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub

...