000			02131 a2200217 4500
005			20190930155716.0
008			190926b xxu||||| |||| 00| 0 eng d
020			_a9781788834247
040			_cIIT Kanpur
041			_aeng
082			_a006.31 _bL311d
100			_aLapan, Maxim
245			_aDeep reinforcement learning hands-on _bapply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, alphago zero and more _cMaxim Lapan
260			_aBirmingham _bPacket Publishing _c2018
300			_axvi, 523p
520			_aKey Features Explore deep reinforcement learning (RL), from the first principles to the latest algorithms Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic algorithms Keep up with the very latest industry developments, including AI-driven chatbots Book Description Recent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google's use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace. Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
650			_aMachine learning
650			_aReinforcement learning
650			_aNatural language processing (Computer science)
942			_cBK
999			_c560553 _d560553