Title:
Learning task performance in market-based task allocation
Learning task performance in market-based task allocation
Author(s)
Pippin, Charles, E.
Christensen, Henrik I.
Christensen, Henrik I.
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Abstract
Auction based algorithms offer effective methods for de-centralized task assignment in multi-agent teams. Typically there is an implicit assumption that agents can be trusted to effectively perform assigned tasks. However, reliable performance of team members may not always be a valid assumption. An approach to learning team member performance is presented, which enables more efficient task assignment. A policy gradient reinforcement learning algorithm is used to learn a cost factor that can be applied individually to auction bids. Experimental results demonstrate that agents that model team member performance using this approach can more efficiently distribute tasks in multi-agent auctions.
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Date Issued
2012-06
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Text
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Proceedings
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Post-print