Implementation Details

This section covers important architectural and implementation choices in RL-A2A.

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Core Components

• Policy Networks: Implemented using PyTorch. Supports MLPs, CNNs, or custom architectures.
• Value Networks: Separate or shared heads for value estimation.
• Replay Buffers: (If used) Experience replay for off-policy methods.
• Optimizers: Configurable (Adam, RMSProp, etc.).
• Schedulers: Learning rate scheduling supported.

Logging & Monitoring

• Uses TensorBoard or Weights & Biases for visualization.
• Logs: Rewards, losses, hyperparameters, evaluation metrics.

Modular Design

• Easily add new algorithms or environments.
• Config-driven experimentation.

Coding Standards

• Type annotations and docstrings are used throughout.
• Unit tests cover all critical modules.

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For more details, see the code in the algorithms/ and agents/ directories.