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020 _a9783031791673
_9978-3-031-79167-3
024 7 _a10.1007/978-3-031-79167-3
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aOsborne, Philip.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_988208
245 1 0 _aApplying Reinforcement Learning on Real-World Data with Practical Examples in Python
_h[electronic resource] /
_cby Philip Osborne, Kajal Singh, Matthew E. Taylor.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aXVII, 92 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
505 0 _aBackground and Definitions -- Reinforcement Learning Theory -- A Robot Cleaner Example -- The Classroom Environment -- Industry Applications -- Conclusion -- Bibliography -- Authors' Biographies.
520 _aReinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve long-term goals. In some cases, this machine learning approach can save programmers time, outperform existing controllers, reach super-human performance, and continually adapt to changing conditions. This book argues that these successes show reinforcement learning can be adopted successfully in many different situations, including robot control, stock trading, supply chain optimization, and plant control. However, reinforcement learning has traditionally been limited to applications in virtual environments or simulations in which the setup is already provided. Furthermore, experimentation may be completed for an almost limitless number of attempts risk-free. In many real-life tasks, applying reinforcement learning is not as simple as (1) data is not in the correct form for reinforcement learning, (2) data is scarce, and (3) automation has limitations in the real-world. Therefore, this book is written to help academics, domain specialists, and data enthusiast alike to understand the basic principles of applying reinforcement learning to real-world problems. This is achieved by focusing on the process of taking practical examples and modeling standard data into the correct form required to then apply basic agents. To further assist with readers gaining a deep and grounded understanding of the approaches, the book shows hand-calculated examples in full and then how this can be achieved in a more automated manner with code. For decision makers who are interested in reinforcement learning as a solution but are not technically proficient we include simple, non-technical examples in the introduction and case studies section. These provide context of what reinforcement learning offer but also the challenges and risks associated with applying it in practice. Specifically, the book illustrates the differences between reinforcement learning and other machine learning approaches as well as how well-known companies have found success using the approach to their problems.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_988209
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aSingh, Kajal.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_988212
700 1 _aTaylor, Matthew E.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_988213
710 2 _aSpringerLink (Online service)
_988216
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031791680
776 0 8 _iPrinted edition:
_z9783031791666
776 0 8 _iPrinted edition:
_z9783031791697
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_988217
856 4 0 _uhttps://doi.org/10.1007/978-3-031-79167-3
912 _aZDB-2-SXSC
942 _cEBK
999 _c86216
_d86216