deep reinforcement learning hands-on 2nd edition maksim lapan.pdf ahrc

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deep reinforcement learning hands-on 2nd edition maksim lapan.pdf

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"Deep Reinforcement Learning Hands-On" (2nd Edition) by Maxim Lapan is a comprehensive guide that explores the theory and practical implementation of deep reinforcement learning (DRL) using Python and PyTorch. The book covers a wide range of algorithms, from foundational concepts to advanced topics, making it accessible to both beginners and experienced practitioners in the field of artificial intelligence and machine learning. It provides hands-on exercises, examples, and projects to help readers build DRL models, with detailed explanations of how neural networks can be used in combination with reinforcement learning techniques like Q-learning, Deep Q Networks (DQN), and Policy Gradient methods. The 2nd edition includes updated content on state-of-the-art methods, such as Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Curiosity-driven learning, reflecting the latest advancements in DRL. By walking through step-by-step implementations, the book helps users grasp key challenges like exploration vs. exploitation, reward shaping, and sample efficiency. It also demonstrates how DRL is applied in real-world domains, such as gaming (e.g., Atari games), robotics, finance, and autonomous driving. Lapan's approach emphasizes a balance between understanding the theory behind DRL and gaining practical experience by coding algorithms from scratch, thus equipping readers with the tools to solve complex decision-making tasks in dynamic environments. This second edition is highly regarded for being both approachable and in-depth, making it a valuable resource for anyone looking to master DRL techniques.