Speeding-up reinforcement learning through abstraction and transfer learning

Marcelo Li Koga, Valdinei Freire Da Silva, Fabio Gagliardi Cozman, Anna Helena Reali Costa

Research output: Contribution to conferenceConference Paper

12 Scopus citations

Abstract

We are interested in the following general question: is it possible to abstract knowledge that is generated while learning the solution of a problem, so that this abstraction can accelerate the learning process? Moreover, is it possible to transfer and reuse the acquired abstract knowledge to accelerate the learning process for future similar tasks? We propose a framework for conducting simultaneously two levels of reinforcement learning, where an abstract policy is learned while learning of a concrete policy for the problem, such that both policies are refined through exploration and interaction of the agent with the environment. We explore abstraction both to accelerate the learning process for an optimal concrete policy for the current problem, and to allow the application of the generated abstract policy in learning solutions for new problems. We report experiments in a robot navigation environment that show our framework to be effective in speeding up policy construction for practical problems and in generating abstractions that can be used to accelerate learning in new similar problems. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
Original languageAmerican English
Pages119-126
Number of pages8
StatePublished - 1 Jan 2013
Externally publishedYes
Event12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 -
Duration: 1 Jan 2013 → …

Conference

Conference12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013
Period1/01/13 → …

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