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Reinforcement Learning Hands-on: Code, Train, and Deploy Intelligent Agents.
Paperback
Publisher: Independently Published
Published: Jun 23 2025
Pages: 210
Weight: 0.63
Height: 0.44 Width: 6.00 Depth: 9.00
Language: English
This book provides a comprehensive, structured overview of reinforcement learning (RL), divided into four parts: foundations, core algorithms, advanced topics, and practical applications.
Part I: Foundations
Lays the groundwork for RL by introducing its core concepts and mathematical background. It covers:
What RL is and where it's applied (games, robotics, trading, etc.)
Mathematical essentials: probability, linear algebra, and optimization
Multi-armed bandits: simple decision-making problems with exploration strategies like ε-greedy, UCB, and Thompson Sampling
Markov Decision Processes (MDPs): the formal framework behind RL, including states, actions, rewards, transitions, and value functions
Dynamic Programming: algorithms like value iteration and policy iteration that solve MDPs when models are known
Part II: Core Algorithms
Focuses on model-free RL methods that learn from experience without full knowledge of the environment:
Monte Carlo Methods: learning from episode returns (first-visit vs. every-visit)
Temporal-Difference Learning: TD(0), SARSA, and Q-learning for online updates
n-Step Methods & TD(λ): blending Monte Carlo and TD approaches for more flexible credit assignment
Policy Gradient Methods: directly optimizing the policy using REINFORCE, baselines, and actor-critic architectures
Part III: Advanced Topics
Covers modern techniques and extensions used in cutting-edge RL systems:
Function Approximation: using linear models or neural networks to scale RL to large or continuous spaces
Deep Reinforcement Learning: deep Q-networks (DQN), experience replay, target networks, Double DQN, and Dueling DQN
Advanced Policy Gradients: including PPO, TRPO, and Soft Actor-Critic (SAC)
Exploration Techniques: intrinsic motivation, curiosity-driven learning, and count-based methods
Multi-Agent RL: handling environments with multiple learning agents-cooperative, competitive, and with communication
Part IV: Practical RL
Equips readers with real-world tools and insights for applying RL:
Training Tips: how to debug RL agents, design reward functions, and tune hyperparameters
Tools & Frameworks: walkthroughs of OpenAI Gym, Stable Baselines, and RLlib
Case Studies: real-world RL applications in game playing (Atari, Go), robotics (OpenAI Dactyl), finance (J.P. Morgan), and autonomous driving (Wayve)
Future Directions: exploration of meta-RL, offline RL, transfer learning, generalization, and ethics/safety in RL deployments
Conclusion
This book balances mathematical depth with hands-on application. It's designed for students, engineers, and researchers looking to understand how reinforcement learning works, how to implement it, and how to apply it in real-world scenarios.
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