--- library_name: stable-baselines3 tags: - Hopper-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Hopper-v3 type: Hopper-v3 metrics: - type: mean_reward value: 161.03 +/- 1.07 name: mean_reward verified: false --- # **TQC** Agent playing **Hopper-v3** This is a trained model of a **TQC** agent playing **Hopper-v3** 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 Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env Hopper-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Hopper-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env Hopper-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Hopper-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env Hopper-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env Hopper-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('top_quantiles_to_drop_per_net', 5), ('normalize', False)]) ```