The most comprehensive RL overview I've ever seen. Kevin Murphy from Google DeepMind, who has over 128k citations, wrote this. What makes this different from other RL resources: → It bridges classical RL with the modern LLM era: There's an entire chapter dedicated to "LLMs and RL" covering: - RLHF, RLAIF, and reward modeling - PPO, GRPO, DPO, RLOO, REINFORCE++ - Training reasoning models - Multi-turn RL for agents - Test-time compute scaling → The fundamentals are crystal clear Every major algorithm, like value-based methods, policy gradients, and actor-critic are explained with mathematical rigor. → Model-based RL and world models get proper coverage Covers Dreamer, MuZero, MCTS, and beyond, which is exactly where the field is heading. → Multi-agent RL section Game theory, Nash equilibrium, and MARL for LLM agents. I have shared the arXiv paper in the replies!