Reinforcement Learning
stable-baselines3
Hopper-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use qgallouedec/tqc-Hopper-v3-1346000078 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use qgallouedec/tqc-Hopper-v3-1346000078 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="qgallouedec/tqc-Hopper-v3-1346000078", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
| 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: 3726.60 +/- 10.89 | |
| 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<br/> | |
| SB3: https://github.com/DLR-RM/stable-baselines3<br/> | |
| 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)]) | |
| ``` | |