README.md

metadata

library_name: stable-baselines3
tags:
  - LunarLander-v2
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
model-index:
  - name: PPO
    results:
      - metrics:
          - type: mean_reward
            value: 280.00 +/- 24.62
            name: mean_reward
        task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: LunarLander-v2
          type: LunarLander-v2

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)

model = PPO(
  policy = 'MlpPolicy',
  env = env,
  n_steps = 2048,
  batch_size = 512,
  n_epochs = 4,
  gamma = 0.099,
  gae_lambda = 0.98,
  ent_coef = 0.01,
  learning_rate=0.00001,
  verbose=1,
  tensorboard_log="./ppo_tensorboard/")
  
model.learn(total_timesteps=int(10e6))