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Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more /: apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more. (2020)
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Book
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Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more /: apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more. (2020)
Main Title:
Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more
Table of ContentsWhat Is Reinforcement Learning?OpenAI GymDeep Learning with PyTorchThe Cross-Entropy MethodTabular Learning and the Bellman EquationDeep Q-NetworksHigher-Level RL librariesDQN ExtensionsWays to Speed up RLStocks Trading Using RLPolicy Gradients – an AlternativeThe Actor-Critic MethodAsynchronous Advantage Actor-CriticTraining Chatbots with RLThe TextWorld environmentWeb NavigationContinuous Action SpaceRL in RoboticsTrust Regions – PPO, TRPO, ACKTR, and SACBlack-Box Optimization in RLAdvanced explorationBeyond Model-Free – ImaginationAlphaGo ZeroRL in Discrete OptimisationMulti-agent RL.
Note: Includes bibliographical references and index. Note: Description based on CIP data; resource not viewed.
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