README.md

metadata

library_name: stable-baselines3
tags:
  - MountainCar-v0
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
model-index:
  - name: QRDQN
    results:
      - metrics:
          - type: mean_reward
            value: '-106.50 +/- 10.22'
            name: mean_reward
        task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: MountainCar-v0
          type: MountainCar-v0

QRDQN Agent playing MountainCar-v0

This is a trained model of a QRDQN agent playing MountainCar-v0 using the stable-baselines3 library and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

Usage (with SB3 RL Zoo)

RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo
SB3: https://github.com/DLR-RM/stable-baselines3
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib

# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo qrdqn --env MountainCar-v0 -orga sb3 -f logs/
python enjoy.py --algo qrdqn --env MountainCar-v0  -f logs/

Training (with the RL Zoo)

python train.py --algo qrdqn --env MountainCar-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo qrdqn --env MountainCar-v0 -f logs/ -orga sb3

Hyperparameters

OrderedDict([('batch_size', 128),
             ('buffer_size', 10000),
             ('exploration_final_eps', 0.07),
             ('exploration_fraction', 0.2),
             ('gamma', 0.98),
             ('gradient_steps', 8),
             ('learning_rate', 0.004),
             ('learning_starts', 1000),
             ('n_timesteps', 120000.0),
             ('policy', 'MlpPolicy'),
             ('policy_kwargs', 'dict(net_arch=[256, 256], n_quantiles=25)'),
             ('target_update_interval', 600),
             ('train_freq', 16),
             ('normalize', False)])