Title:
Learning Roles: Behavioral Diversity in Robot Teams
Learning Roles: Behavioral Diversity in Robot Teams
dc.contributor.author | Balch, Tucker | en_US |
dc.date.accessioned | 2005-06-17T17:51:21Z | |
dc.date.available | 2005-06-17T17:51:21Z | |
dc.date.issued | 1997 | en_US |
dc.description.abstract | This paper describes research investigating behavioral specialization in learning robot teams. Each agent is provided a common set of skills (motor schema-based behavioral assemblages) from which it builds a task-achieving strategy using reinforcement learning. The agents learn individually to activate particular behavioral assemblages given their current situation and a reward signal. The experiments, conducted in robot soccer simulations, evaluate the agents in terms of performance, policy convergence, and behavioral diversity. The results show that in many cases, robots will automatically diversify by choosing heterogeneous behaviors. The degree of diversification and the performance of the team depend on the reward structure. When the entire team is jointly rewarded or penalized (global reinforcement), teams tend towards heterogeneous behavior. When agents are provided feedback individually (local reinforcement), they converge to identical policies. | en_US |
dc.format.extent | 201228 bytes | |
dc.format.mimetype | application/pdf | |
dc.identifier.uri | http://hdl.handle.net/1853/6647 | |
dc.language.iso | en_US | |
dc.publisher | Georgia Institute of Technology | en_US |
dc.relation.ispartofseries | CC Technical Report; GIT-CC-97-12 | en_US |
dc.subject | Machine learning | |
dc.subject | Multi-robot teams | |
dc.subject | Reinforcement learning | |
dc.subject | Global reinforcement | |
dc.title | Learning Roles: Behavioral Diversity in Robot Teams | 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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