| tags: | |
| - CartPole-v1 | |
| - reinforce | |
| - reinforcement-learning | |
| - custom-implementation | |
| - deep-rl-class | |
| model-index: | |
| - name: DeepRL | |
| 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 | |
| # **ppo** Agent playing **PushBlock** | |
| This is a trained model of a **ppo** agent playing **PushBlock** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). | |
| ## Usage (with ML-Agents) | |
| The Documentation: https://github.com/huggingface/ml-agents#get-started | |
| We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: | |
| ### Resume the training | |
| ``` | |
| mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume | |
| ``` | |
| ### Watch your Agent play | |
| You can watch your agent **playing directly in your browser:**. | |
| 1. Go to https://huggingface.co/spaces/unity/ML-Agents-PushBlock | |
| 2. Step 1: Write your model_id: jackoyoungblood/DeepRL | |
| 3. Step 2: Select your *.nn /*.onnx file | |
| 4. Click on Watch the agent play 👀 | |