| tags: | |
| - Pixelcopter-PLE-v0 | |
| - reinforce | |
| - reinforcement-learning | |
| - custom-implementation | |
| - deep-rl-class | |
| model-index: | |
| - name: REINFORCE-PixelCopter | |
| results: | |
| - task: | |
| type: reinforcement-learning | |
| name: reinforcement-learning | |
| dataset: | |
| name: Pixelcopter-PLE-v0 | |
| type: Pixelcopter-PLE-v0 | |
| metrics: | |
| - type: mean_reward | |
| value: 6.50 +/- 5.99 | |
| name: mean_reward | |
| verified: false | |
| # **Reinforce** Agent playing **Pixelcopter-PLE-v0** | |
| This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . | |
| Portable weights: `model_state_dict.pt`. Define the matching Unit 4 Policy | |
| (CartPole: two layers; PixelCopter: three layers), instantiate it with | |
| `state_space`, `action_space`, and `h_size` from `hyperparameters.json`, then | |
| use `policy.load_state_dict(torch.load(weights_path, map_location="cpu", weights_only=True))`. | |
| The legacy `model.pt` contains a full Python model and requires its matching class. | |
| To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction | |