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: 282.88 +/- 14.89
            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)

  from typing import Callable

  def linear_schedule(initial_value: float) -> Callable[[float], float]:
      def func(progress_remaining: float) -> float:
          return progress_remaining * initial_value
  
      return func
  
  model = PPO(policy="MlpPolicy", env=env, verbose=1, n_epochs=10, learning_rate=linear_schedule(0.005), n_steps=1500)