Reinforcement Learning Module
Structure
The reinforcement learning module is meant to have the code necessary to train and evaluate RL agents. It has the following structure:
smart_control/reinforcement_learning/
├── agents/ # RL agent implementations (SAC, TD3, DDPG)
│ └── networks/ # Neural networks for agents
├── observers/ # Monitoring and data collection during training/evaluation
├── policies/ # Policy implementations (including baseline policies)
├── replay_buffer/ # Experience replay buffer management
├── scripts/ # Training and evaluation scripts
├── utils/ # Utility functions and helpers
└── visualization/ # Visualization tools for analysis
Tutorials
Check out this tutorial to help get started with the RL module.