--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 136.79 +/- 42.72 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **DQN** Agent playing **LunarLander-v2** This is a trained model of a **DQN** agent playing **LunarLander-v2** 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 rl_zoo3.load_from_hub --algo dqn --env LunarLander-v2 -orga sb3 -f logs/ python enjoy.py --algo dqn --env LunarLander-v2 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env LunarLander-v2 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env LunarLander-v2 -f logs/ -orga sb3 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 128), ('buffer_size', 50000), ('exploration_final_eps', 0.1), ('exploration_fraction', 0.12), ('gamma', 0.99), ('gradient_steps', -1), ('learning_rate', 0.00063), ('learning_starts', 0), ('n_timesteps', 100000.0), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(net_arch=[256, 256])'), ('target_update_interval', 250), ('train_freq', 4), ('normalize', False)]) ```