Deep reinforcement learning hands-on : : apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more / / Maxim Lapan.

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Place / Publishing House:Birmingham, England : : Packt Publishing,, 2018.
Year of Publication:2018
Language:English
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Physical Description:1 online resource (547 pages)
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100 1 |a Lapan, Maxim,  |e author. 
245 1 0 |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. 
264 1 |a Birmingham, England :  |b Packt Publishing,  |c 2018. 
300 |a 1 online resource (547 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
588 |a Description based on print version record. 
590 |a Electronic reproduction. Ann Arbor, MI : ProQuest, 2018. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries. 
650 0 |a Reinforcement learning. 
655 4 |a Electronic books. 
776 0 8 |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, England : Packt Publishing, c2018   |h 547 pages   |z 9781788834247 
797 2 |a ProQuest (Firm) 
856 4 0 |u https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=5434975  |z Click to View