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Improving reinforcement learning by using case based heuristics

  • Reinaldo A.C. Bianchi
  • , Raquel Ros
  • , Ramon Lopez De Mantaras

Research output: Book chapterConference contributionpeer-review

35 Citations (Scopus)

Abstract

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 Reinforcement Learning (RL) techniques. This approach, called Case Based Heuristically Accelerated Reinforcement Learning (CB-HARL), builds upon an emerging technique, the Heuristic Accelerated Reinforcement Learning (HARL), in which RL methods are accelerated by making use of heuristic information. CB-HARL is a subset of RL that makes use of a heuristic function derived from a case base, in a Case Based Reasoning manner. An algorithm that incorporates CBR techniques into the Heuristically Accelerated Q-Learning is also proposed. Empirical evaluations were conducted in a simulator for the RoboCup Four-Legged Soccer Competition, and results obtained shows that using CB-HARL, the agents learn faster than using either RL or HARL methods.

Original languageEnglish
Title of host publicationCase-Based Reasoning Research and Development - 8th International Conference on Case-Based Reasoning, ICCBR 2009, Proceedings
Pages75-89
Number of pages15
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event8th International Conference on Case-Based Reasoning, ICCBR 2009 - Seattle, WA, United States
Duration: 20 Jul 200923 Jul 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5650 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Case-Based Reasoning, ICCBR 2009
Country/TerritoryUnited States
CitySeattle, WA
Period20/07/0923/07/09

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