Title:
Clay: Integrating Motor Schemas and Reinforcement Learning

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dc.contributor.author Balch, Tucker en_US
dc.date.accessioned 2005-06-17T17:51:15Z
dc.date.available 2005-06-17T17:51:15Z
dc.date.issued 1997 en_US
dc.description.abstract Clay is an evolutionary architecture for autonomous robots that integrates motor schema-based control and reinforcement learning. Robots utilizing Clay benefit from the real-time performance of motor schemas in continuous and dynamic environments while taking advantage of adaptive reinforcement learning. Clay coordinates assemblages (groups of motor schemas) using embedded reinforcement learning modules. The coordination modules activate specific assemblages based on the presently perceived situation. Learning occurs as the robot selects assemblages and samples a reinforcement signal over time. Experiments in a robot soccer simulation illustrate the performance and utility of the system. en_US
dc.format.extent 384322 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/6646
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.relation.ispartofseries CC Technical Report; GIT-CC-97-11 en_US
dc.subject Robot control application program
dc.subject Motor schemas
dc.subject Reinforcement learning
dc.title Clay: Integrating Motor Schemas and Reinforcement Learning en_US
dc.type Text
dc.type.genre Technical Report
dspace.entity.type Publication
local.contributor.corporatename College of Computing
local.relation.ispartofseries College of Computing Technical Report Series
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isSeriesOfPublication 35c9e8fc-dd67-4201-b1d5-016381ef65b8
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