000 03417nam a22005055i 4500
001 978-3-319-01168-4
003 DE-He213
005 20200420220227.0
007 cr nn 008mamaa
008 130623s2013 gw | s |||| 0|eng d
020 _a9783319011684
_9978-3-319-01168-4
024 7 _a10.1007/978-3-319-01168-4
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aHester, Todd.
_eauthor.
245 1 0 _aTEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
_h[electronic resource] /
_cby Todd Hester.
264 1 _aHeidelberg :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aXIV, 165 p. 55 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v503
505 0 _aIntroduction -- Background and Problem Specification -- Real Time Architecture -- The TEXPLORE Algorithm -- Empirical Evaluation -- Further Examination of Exploration -- Related Work -- Discussion and Conclusion -- TEXPLORE Pseudo-Code.
520 _aThis book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuous state features; 3) it must handle sensor and/or actuator delays; and 4) it should continually select actions in real time. This book focuses on addressing all four of these challenges. In particular, this book is focused on time-constrained domains where the first challenge is critically important. In these domains, the agent's lifetime is not long enough for it to explore the domains thoroughly, and it must learn in very few samples.
650 0 _aEngineering.
650 0 _aImage processing.
650 0 _aComputational intelligence.
650 0 _aRobotics.
650 0 _aAutomation.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aRobotics and Automation.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319011677
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v503
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-01168-4
912 _aZDB-2-ENG
942 _cEBK
999 _c52280
_d52280