Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto, 2018 (MIT Press) - Provides a comprehensive foundation in reinforcement learning theory, algorithms, and concepts, for understanding the principles behind RL agents.
TF-Agents: Reinforcement Learning in TensorFlow, TensorFlow Authors, 2024 - The official guide to using the TF-Agents library, describing its components, API, and implementation patterns for building RL systems. Accessed current year.
Human-level control through deep reinforcement learning, Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller, 2015Nature, Vol. 518DOI: 10.1038/nature14236 - A paper introducing Deep Q-Networks (DQN), showing how deep learning can be combined with Q-learning to achieve high performance in environments.