Reinforcement Learning in Action: From Foundations to Frontier AI

Reinforcement learning (RL) has become the engine behind some of the most significant advances in modern artificial intelligence, from defeating world champions in Go to aligning large language models with human preferences.Yet despite its central role, RL remains poorly understood by many practitioners who work with these systems daily.Reinforcement Learning in Action: From Foundations to Frontiers bridges the gap between classical RL theory and the cutting-edge techniques driving today’s AI breakthroughs.The book traces a complete path from Markov Decision Processes and Bellman equations through deep RL methods (DQN, REINFORCE, Actor-Critic, PPO) to the modern landscape of LLM alignment (RLHF, DPO, SimPO, KTO), reasoning optimization (GRPO, VinePPO, MCTS), and agentic systems with tool use, memory, and multi-turn planning.A distinguishing feature is the book’s consistent five-layer pedagogical structure: each algorithm is presented with its key characteristics, a full mathematical de