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ICCBR
2009
Springer
13 years 11 months ago
Improving Reinforcement Learning by Using Case Based Heuristics
This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and ...
Reinaldo A. C. Bianchi, Raquel Ros, Ramon Ló...
ECAI
2010
Springer
13 years 5 months ago
Case-Based Multiagent Reinforcement Learning: Cases as Heuristics for Selection of Actions
This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Multiagent Reinforcement Learning algorithms, combining Case-Based Reasoning...
Reinaldo A. C. Bianchi, Ramon López de M&aa...
IJAMCIGI
2010
90views more  IJAMCIGI 2010»
13 years 1 months ago
A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling
Hyper-heuristics are identified as the methodologies that search the space generated by a finite set of low level heuristics for solving difficult problems. One of the iterative h...
Ender Özcan, Mustafa Misir, Gabriela Ochoa, E...
EWCBR
2008
Springer
13 years 6 months ago
Recognizing the Enemy: Combining Reinforcement Learning with Strategy Selection Using Case-Based Reasoning
This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve...
Bryan Auslander, Stephen Lee-Urban, Chad Hogg, H&e...
IEEEPACT
2008
IEEE
13 years 11 months ago
Feature selection and policy optimization for distributed instruction placement using reinforcement learning
Communication overheads are one of the fundamental challenges in a multiprocessor system. As the number of processors on a chip increases, communication overheads and the distribu...
Katherine E. Coons, Behnam Robatmili, Matthew E. T...