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Many algorithms such as Q-learning successfully address reinforcement learning in single-agent multi-time-step problems. In addition there are methods that address reinforcement learning in multi-agent single-time-step problems. However, unmodified single-agent multi-time-step methods and multi-agent single-time-step methods cannot necessarily be combined to solve multi-agent multi-time-step problems due to strong coupling between multi-agent interactions between time steps. Rewards that result in multi-agent collaboration for a single time-step may result in poor collaboration in future time-steps. This paper shows how to avoid this problem.
@inproceedings{tumer-agogino_marl_aamas04,
author = {K. Tumer and A. Agogino},
title = {Time-Extended Policies in Multiagent Reinforcement Learning},
booktitle = {Proceedings of the Third International Joint Conference on
Autonomous Agents and Multiagent Systems},
pages = {1336-1337},
month = {July},
address = {New York, NY},
abstract = {Many algorithms such as Q-learning successfully address reinforcement learning in single-agent multi-time-step problems. In addition there are methods that address reinforcement learning in multi-agent single-time-step problems. However, unmodified single-agent multi-time-step methods and multi-agent single-time-step methods cannot necessarily be combined to solve multi-agent multi-time-step problems due to strong coupling between multi-agent interactions between time steps. Rewards that result in multi-agent collaboration for a single time-step may result in poor collaboration in future time-steps. This paper shows how to avoid this problem.},
bib2html_pubtype = {Refereed Conference Papers},
bib2html_rescat = {Multiagent Systems, Reinforcement Learning},
year = {2004}
}
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