000			01909nam a2200229Ia 4500
003			NULRC
005			20250520102954.0
008			250520s9999 xx 000 0 und d
020			_a9781788834247
040			_cNULRC
050			_aQ 325.5 .L37 2018
100			_aLapan, Maxim _eauthor
245		0	_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, UK : _bPackt Publishing, Limited, _cc2018
300			_axvi, 523 pages ; _c24 cm.
504			_aIncludes index.
505			_aWhat is Reinforcement Learning? -- OpenAI GymDeep Learning with PyTorch -- The Cross-Entropy Method -- Tabular Learning and the Bellman Equation -- Deep Q-Networks -- DQN Extensions -- Stocks Trading Using RL -- Policy Gradients - An Alternative -- The Actor-Critic Method -- Asynchronous Advantage Actor-Critic --Chatbots Training with RL Web Navigation -- Continuous Action Space -- Trust Regions - TRPO, PPO, and ACKTR -- Black-Box Optimization in RL -- Beyond Model-Free - Imagination -- AlphaGo Zero
520			_aDeep 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			_aREINFORCEMENT LEARNING
700			_aLapan, Maxim _eco-author
942			_2lcc _cBK
999			_c20225 _d20225