Deep RL book covers PPO, MuZero, RLHF, reasoning models

Published on arXiv (2608.00133), Ghoshana Bista's book is a comprehensive introduction to deep reinforcement learning that traces the field's evolution from classical dynamic programming and temporal-difference learning to modern reasoning-based models. The early chapters establish core concepts like Markov decision processes, Monte Carlo methods, and the shift from tabular to deep approaches. The middle sections dive into major algorithmic families: value-based methods like DQN, policy gradients, actor-critic methods, PPO, SAC, model-based approaches like MuZero, offline RL, and sequence-modeling techniques.

Later chapters extend into multi-agent learning, safe RL, RLHF, and reasoning-oriented AI systems, tying theory to practical challenges. Real-world case studies include UAV-assisted networks, SD-WAN traffic engineering, safe control, and reasoning-based AI, tackling partial observability, safety constraints, and deployment drift. The book is designed for students, researchers, and engineers with basic probability, linear algebra, calculus, and programming knowledge, offering both theoretical depth and systems perspective for building robust RL applications.