| env_name: Walker2d-v5 | |
| tags: | |
| - Walker2d-v5 | |
| - sac | |
| - reinforcement-learning | |
| - custom-implementation | |
| - policy-gradient | |
| - pytorch | |
| - ddpg | |
| model-index: | |
| - name: SAC-Walker2dV5 | |
| results: | |
| - task: | |
| type: reinforcement-learning | |
| name: reinforcement-learning | |
| dataset: | |
| name: Walker2d-v5 | |
| type: Walker2d-v5 | |
| metrics: | |
| - type: mean_reward | |
| value: 4150.91 +/- 823.47 | |
| name: mean_reward | |
| verified: false | |
| # **SAC** Agent playing **Walker2d-v5** | |
| This is a trained model of a **SAC** agent playing **Walker2d-v5**. | |
| ## Usage | |
| ### create the conda env in https://github.com/GeneHit/drl_practice | |
| ```bash | |
| conda create -n drl python=3.10 | |
| conda activate drl | |
| python -m pip install -r requirements.txt | |
| ``` | |
| ### play with full model | |
| ```python | |
| # load the full model | |
| model = load_from_hub(repo_id="winkin119/SAC-Walker2dV5", filename="full_model.pt") | |
| # Create the environment. | |
| env = gym.make("Walker2d-v5") | |
| state, _ = env.reset() | |
| action = model.action(state) | |
| ... | |
| ``` | |
| There is also a state dict version of the model. | |