| env_name: CartPole-v1 | |
| tags: | |
| - CartPole-v1 | |
| - a3c with gae | |
| - reinforcement-learning | |
| - custom-implementation | |
| - policy-gradient | |
| - pytorch | |
| - a3c | |
| - gae | |
| model-index: | |
| - name: A3C-CartPoleV1 | |
| results: | |
| - task: | |
| type: reinforcement-learning | |
| name: reinforcement-learning | |
| dataset: | |
| name: CartPole-v1 | |
| type: CartPole-v1 | |
| metrics: | |
| - type: mean_reward | |
| value: 500.00 +/- 0.00 | |
| name: mean_reward | |
| verified: false | |
| # **A3C with GAE** Agent playing **CartPole-v1** | |
| This is a trained model of a **A3C with GAE** agent playing **CartPole-v1**. | |
| ## 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/A3C-CartPoleV1", filename="full_model.pt") | |
| # Create the environment. | |
| env = gym.make("CartPole-v1") | |
| state, _ = env.reset() | |
| action = model.action(state) | |
| ... | |
| ``` | |
| There is also a state dict version of the model, you can check the corresponding definition in the repo. | |