| LDR | | 03293cmm u2200577Ii 4500 |
| 001 | | 000000312438 |
| 003 | | OCoLC |
| 005 | | 20230525152248 |
| 006 | | m d |
| 007 | | cr unu|||||||| |
| 008 | | 180731s2018 enka ob 001 0 eng d |
| 019 | |
▼a 1042318736 |
| 020 | |
▼a 9781788839303 |
| 020 | |
▼a 1788839307 |
| 020 | |
▼z 9781788834247 |
| 035 | |
▼a 1837369
▼b (N$T) |
| 035 | |
▼a (OCoLC)1046682461
▼z (OCoLC)1042318736 |
| 037 | |
▼a CL0500000982
▼b Safari Books Online |
| 040 | |
▼a UMI
▼b eng
▼e rda
▼e pn
▼c UMI
▼d STF
▼d TOH
▼d OCLCF
▼d EBLCP
▼d N$T
▼d MERUC
▼d ZCU
▼d NLE
▼d TEFOD
▼d CEF
▼d 248032 |
| 049 | |
▼a MAIN |
| 050 | 4 |
▼a Q325.5 |
| 072 | 7 |
▼a COM
▼x 000000
▼2 bisacsh |
| 082 | 04 |
▼a 006.31
▼2 23 |
| 100 | 1 |
▼a Lapan, Maxim,
▼e author. |
| 245 | 10 |
▼a Deep reinforcement learning hands-on :
▼b apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more /
▼c Maxim Lapan. |
| 260 | |
▼a Birmingham, UK :
▼b Packt Publishing,
▼c 2018. |
| 300 | |
▼a 1 online resource (1 volume) :
▼b illustrations |
| 336 | |
▼a text
▼b txt
▼2 rdacontent |
| 337 | |
▼a computer
▼b c
▼2 rdamedia |
| 338 | |
▼a online resource
▼b cr
▼2 rdacarrier |
| 500 | |
▼a "Expert insight." |
| 504 | |
▼a Includes bibliographical references and index. |
| 505 | 0 |
▼a Table of ContentsWhat is Reinforcement Learning?OpenAI GymDeep Learning with PyTorchThe Cross-Entropy MethodTabular Learning and the Bellman EquationDeep Q-NetworksDQN ExtensionsStocks Trading Using RLPolicy Gradients - An AlternativeThe Actor-Critic MethodAsynchronous Advantage Actor-CriticChatbots Training with RL Web NavigationContinuous Action SpaceTrust Regions - TRPO, PPO, and ACKTRBlack-Box Optimization in RLBeyond Model-Free - ImaginationAlphaGo Zero. |
| 520 | |
▼a This book is a practical, developer-oriented introduction to deep reinforcement learning (RL). Explore the theoretical concepts of RL, before discovering how deep learning (DL) methods and tools are making it possible to solve more complex and challenging problems than ever before. Apply deep RL methods to training your agent to beat arcade ... |
| 588 | |
▼a Description based on online resource; title from cover (Safari, viewed July 30, 2018). |
| 590 | |
▼a Master record variable field(s) change: 072, 082 - OCLC control number change |
| 650 | 0 |
▼a Reinforcement learning. |
| 650 | 0 |
▼a Machine learning. |
| 650 | 0 |
▼a Natural language processing (Computer science) |
| 650 | 0 |
▼a Artificial intelligence. |
| 650 | 7 |
▼a Artificial intelligence.
▼2 fast
▼0 (OCoLC)fst00817247 |
| 650 | 7 |
▼a Machine learning.
▼2 fast
▼0 (OCoLC)fst01004795 |
| 650 | 7 |
▼a Natural language processing (Computer science)
▼2 fast
▼0 (OCoLC)fst01034365 |
| 650 | 7 |
▼a Reinforcement learning.
▼2 fast
▼0 (OCoLC)fst01732553 |
| 650 | 7 |
▼a COMPUTERS / General.
▼2 bisacsh |
| 655 | 4 |
▼a Electronic books. |
| 776 | 08 |
▼i Print version:
▼a Lapan, Maxim
▼t Deep Reinforcement Learning Hands-On : Apply Modern RL Methods, with Deep Q-Networks, Value Iteration, Policy Gradients, TRPO, AlphaGo Zero and More
▼d Birmingham : Packt Publishing Ltd,c2018
▼z 9781788834247 |
| 856 | 40 |
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1837369 |
| 938 | |
▼a EBL - Ebook Library
▼b EBLB
▼n EBL5434975 |
| 938 | |
▼a EBSCOhost
▼b EBSC
▼n 1837369 |
| 990 | |
▼a 관리자 |
| 994 | |
▼a 92
▼b N$T |