| | --- |
| | library_name: ml-agents |
| | tags: |
| | - 3d-ball |
| | - deep-reinforcement-learning |
| | - reinforcement-learning |
| | - ppo |
| | - unity-ml-agents |
| | --- |
| | |
| | # 3DBall Trained Agent |
| |
|
| | This is a trained model of a PPO agent playing the 3DBall environment, created using the Unity ML-Agents library. The agent learns to balance a ball on a moving platform for as long as possible. |
| |
|
| | ### Training Hyperparameters |
| |
|
| | The agent was trained using the following configuration from the `3DBall.yaml` file: |
| |
|
| | ```yaml |
| | behaviors: |
| | 3DBall: |
| | trainer_type: ppo |
| | hyperparameters: |
| | learning_rate: 0.0003 |
| | learning_rate_schedule: linear |
| | beta: 0.0005 |
| | epsilon: 0.2 |
| | lambd: 0.95 |
| | num_epoch: 3 |
| | buffer_size: 2048 |
| | batch_size: 256 |
| | time_horizon: 1024 |
| | network_settings: |
| | normalize: false |
| | hidden_units: 128 |
| | num_layers: 2 |
| | vis_encode_type: simple |
| | reward_signals: |
| | extrinsic: |
| | gamma: 0.99 |
| | strength: 1.0 |
| | checkpoint_interval: 500000 |
| | threaded: true |
| | ``` |
| |
|
| | ### Video Demo |
| |
|
| | Here is a video of the trained agent in action, demonstrating the learned behavior. |
| |
|
| | <video controls width="100%"> |
| | <source src="3DBall_Demo.mp4" type="video/mp4"> |
| | Your browser does not support the video tag. |
| | </video> |
| |
|
