--- library_name: stable-baselines3 tags: - MountainCar-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: -103.40 +/- 7.49 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: MountainCar-v0 type: MountainCar-v0 --- # **DQN** Agent playing **MountainCar-v0** This is a trained model of a **DQN** agent playing **MountainCar-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-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 utils.load_from_hub --algo dqn --env MountainCar-v0 -orga sb3 -f logs/ python enjoy --algo dqn --env MountainCar-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env MountainCar-v0 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env MountainCar-v0 -f logs/ -orga sb3 ``` ## Hyperparameters ```python 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])'), ('target_update_interval', 600), ('train_freq', 16), ('normalize', False)]) ```