Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Deep Reinforcement Learning in Practice: Build Real-World AI Agents with PyTorch, PPO, and RLHF

Deep Reinforcement Learning in Practice: Build Real-World AI Agents with PyTorch, PPO, and RLHF

Westfield, Frank

Paperback

General ComputersProgramming

ISBN13: 9798257999741
Publisher: Independently Published
Published: Apr 19 2026
Pages: 136
Weight: 0.44
Height: 0.29 Width: 6.14 Depth: 9.21
Language: English
Deep Reinforcement Learning in Practice: Build Real-World AI Agents with PyTorch, PPO, and RLHF is a practical and deeply structured guide designed to take you from foundational reinforcement learning concepts to the point where you can confidently design, train, evaluate, and deploy intelligent agents in real environments. This book goes beyond theory and isolated algorithms by focusing on how reinforcement learning systems are actually built in modern AI applications, including robotics, financial systems, game environments, autonomous decision-making agents, and large language model alignment through human feedback.Reinforcement learning has become one of the most important pillars of artificial intelligence, powering systems that learn from interaction rather than static datasets. However, many learners struggle to move from understanding the mathematics to implementing systems that actually work in practice. This book solves that problem by providing a structured learning path that connects core ideas such as Q-learning, policy gradients, and actor-critic methods to advanced techniques like Proximal Policy Optimization, Deep Q-Networks, and Reinforcement Learning from Human Feedback. Every concept is presented with clarity and grounded in real implementation thinking using PyTorch.

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