{"product_id":"foundations-of-deep-reinforcement-learning-theory-and-practice-in-python-9780135172384","title":"Foundations of Deep Reinforcement Learning: Theory and Practice in Python","description":"\u003cb\u003eThe Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eDeep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games-such as Go, Atari games, and DotA 2-to robotics. \u003cp\u003e\u003c\/p\u003e \u003ci\u003e\u003cb\u003eFoundations of Deep Reinforcement Learning\u003c\/b\u003e\u003c\/i\u003e is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. \u003cbr\u003e This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. \u003cul\u003e \u003cli\u003eUnderstand each key aspect of a deep RL problem\u003c\/li\u003e \u003cli\u003eExplore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)\u003c\/li\u003e \u003cli\u003eDelve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)\u003c\/li\u003e \u003cli\u003eUnderstand how algorithms can be parallelized synchronously and asynchronously\u003c\/li\u003e \u003cli\u003eRun algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work\u003c\/li\u003e \u003cli\u003eExplore algorithm benchmark results with tuned hyperparameters\u003c\/li\u003e \u003cli\u003eUnderstand how deep RL environments are designed\u003c\/li\u003e \u003c\/ul\u003e \u003ci\u003eRegister your book for convenient access to downloads, updates, and\/or corrections as they become available. See inside book for details.\u003c\/i\u003e \u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eLaura Graesser\u003c\/b\u003e is a research software engineer working in robotics at Google. She holds a master's degree in computer science from New York University, where she specialized in machine learning. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eWah Loon Keng\u003c\/b\u003e is an AI engineer at Machine Zone, where he applies deep reinforcement learning to industrial problems. He has a background in both theoretical physics and computer science.\u003cbr\u003e","brand":"Addison-Wesley Professional","offers":[{"title":"Default Title","offer_id":50434303623442,"sku":"9780135172384","price":45.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_c3beca49-f04c-431d-8e57-1e642bc7c901.jpg?v=1729598436","url":"https:\/\/surprise-castle.myshopify.com\/products\/foundations-of-deep-reinforcement-learning-theory-and-practice-in-python-9780135172384","provider":"Surprise Castle","version":"1.0","type":"link"}