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001 | u374623 | ||
003 | SIRSI | ||
005 | 20160812084243.0 | ||
007 | cr nn 008mamaa | ||
008 | 100709s2010 gw | s |||| 0|eng d | ||
020 |
_a9783642139321 _9978-3-642-13932-1 |
||
040 | _cMX-MeUAM | ||
050 | 4 | _aQ342 | |
082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aWhiteson, Shimon. _eauthor. |
|
245 | 1 | 0 |
_aAdaptive Representations for Reinforcement Learning _h[recurso electrónico] / _cby Shimon Whiteson. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010. |
|
300 |
_aXIII, 116 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aStudies in Computational Intelligence, _x1860-949X ; _v291 |
|
505 | 0 | _aPart 1 Introduction -- Part 2 Reinforcement Learning -- Part 3 On-Line Evolutionary Computation -- Part 4 Evolutionary Function Approximation -- Part 5 Sample-Efficient Evolutionary Function Approximation -- Part 6 Automatic Feature Selection for Reinforcement Learning -- Part 7 Adaptive Tile Coding -- Part 8 RelatedWork -- Part 9 Conclusion -- Part 10 Statistical Significance. | |
520 | _aThis book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own representations have the potential to dramatically improve performance. This book introduces two novel approaches for automatically discovering high-performing representations. The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for a broad class of optimization problems. This synthesis is accomplished by customizing evolutionary methods to the on-line nature of reinforcement learning and using them to evolve representations for value function approximators. The second approach automatically learns representations based on piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements. This book also introduces a novel method for devising input representations. This method addresses the feature selection problem by extending an algorithm that evolves the topology and weights of neural networks such that it evolves their inputs too. In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methods with manual representations. | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aArtificial intelligence. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aComputational Intelligence. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783642139314 |
830 | 0 |
_aStudies in Computational Intelligence, _x1860-949X ; _v291 |
|
856 | 4 | 0 |
_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-13932-1 |
596 | _a19 | ||
942 | _cLIBRO_ELEC | ||
999 |
_c202503 _d202503 |