README.md

metadata

library_name: stable-baselines3
tags:
  - MountainCarContinuous-v0
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
model-index:
  - name: PPO
    results:
      - metrics:
          - type: mean_reward
            value: '-0.00 +/- 0.00'
            name: mean_reward
        task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: MountainCarContinuous-v0
          type: MountainCarContinuous-v0

PPO Agent playing MountainCarContinuous-v0

This is a trained model of a PPO agent playing MountainCarContinuous-v0 using the stable-baselines3 library.

Usage (with Stable-baselines3)

from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub

# load and create the model
model_path = load_from_hub("danieladejumo/ppo-mountan_car_continuous", 
                           "ppo-mountan_car_continuous.zip")
model = PPO.load(model_path)

# create Mountain Car Continuous environment and evaluate the trained agent
env = gym.make("MountainCarContinuous-v0")
mean_return, std_return = evaluate_policy(model, env, n_eval_episodes=50, deterministic=True)