TRFL

Provides reusable building blocks like Q-learning and policy gradient losses, simplifying RL algorithm development by reducing boilerplate code, as shown in the usage example.

All loss functions return tensors that can be differentiated, enabling seamless gradient-based optimization within TensorFlow computational graphs, as described in the documentation.

Each function returns namedtuples with auxiliary data such as TD errors and targets, aiding in analysis and debugging, as highlighted in the extra information section.

Built specifically for TensorFlow, ensuring compatibility and easy integration into existing TensorFlow pipelines, which aligns with its philosophy of adhering to TensorFlow's paradigm.