Deep Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices - Softcover

Ara, Husn

9798286962884: Deep Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices

Softcover

ISBN 13: 9798286962884

Publisher: Independently published, 2025

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📘 Deep Reinforcement Learning with Python – Book Summary

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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