Erhältlich:
Nicht auf Lager
Buch (Softcover): Fachbuch
Reinforcement Learning Hands-on
Code, Train, and Deploy Intelligent Agents
Verlag:
Independently Published Unsere-Artikel-Nr.: P36540992
EAN: 9798289299260
Erhältlich:
Nicht auf Lager
Zustellung: Fr, 06.11.2026
Versand: Kostenlos
CHF 45.–
Beschreibung
Reinforcement Learning Hands-on: Code, Train, and Deploy Intelligent Agents. 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: FoundationsLays 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 AlgorithmsFocuses 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 TopicsCovers 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 RLEquips 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. ConclusionThis 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.
Spezifikationen
Sprache
- Englisch
Autor
- Husn Ara
Erscheinungsjahr
- 2025
Format
- Buch (Softcover)
Anzahl Seiten
- 210