It was written by Kevin Murphy from Google DeepMind, who has over 128k citations.
How it differs from other materials on RL:
→ There's a bridge between classical RL and the current era of LLMs:
A separate chapter on LLMs and RL, which discusses:
RLHF, RLAIF, and reward modeling
PPO, GRPO, DPO, RLOO, REINFORCE++
Training reasoning models
Multi-turn RL for agents
Scaling computations for inference (test-time compute scaling)
→ The basics are explained very clearly
All major algorithms like value-based methods, policy gradients, and actor-critic are explained with mathematical rigor.
→ Model-based RL and world models are also well-covered
There's Dreamer, MuZero, MCTS, and more on the list - this is exactly where the field is heading now.
→ A section on multi-agent RL
Game theory, Nash equilibrium, and MARL for LLM agents.
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