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In many multiagent domains, and particularly in tightly coupled domains, teasing an agentŐs contribution to the system performance based on a single episodic return is difficult. This well-known difficulty hits state-to-action mapping approaches such as neural net- works trained by evolutionary algorithms particularly hard. This paper introduces fitness critics, which leverage the expected fitness to evaluate an agentŐs performance. This approach turns a sparse performance metric (policy evaluation) into a dense performance metric (state-action evaluation) by relating the episodic feedback to the state-action pairs experienced during the execution of that policy. In the tightly-coupled multi-rover domain (where multiple rovers have to perform a particular task simultaneously), only teams using fitness critics were able to demonstrate effective learning on tasks with tight coupling while other coevolved teams were unable to learn at all.
@InProceedings{tumer-rockefeller_aamas20,
author = {G. Rockefeller and S. Khadka and K. Tumer},
title = {Multi-level Fitness Critics for Cooperative Coevolution},
booktitle = {Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems},
address = {Auckland, New Zealand},
month = {May},
pages={},
abstract={In many multiagent domains, and particularly in tightly coupled domains, teasing an agentÕs contribution to the system performance based on a single episodic return is difficult. This well-known difficulty hits state-to-action mapping approaches such as neural net- works trained by evolutionary algorithms particularly hard. This paper introduces fitness critics, which leverage the expected fitness to evaluate an agentÕs performance. This approach turns a sparse performance metric (policy evaluation) into a dense performance metric (state-action evaluation) by relating the episodic feedback to the state-action pairs experienced during the execution of that policy. In the tightly-coupled multi-rover domain (where multiple rovers have to perform a particular task simultaneously), only teams using fitness critics were able to demonstrate effective learning on tasks with tight coupling while other coevolved teams were unable to learn at all.},
bib2html_pubtype = {Refereed Conference Papers},
bib2html_rescat = {Multiagent Systems, Evolutionary Algorithms},
year = {2020}
}
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