--- 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.