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

Sinopsis

📘 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.

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

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

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

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

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.

"Sinopsis" puede pertenecer a otra edición de este libro.