Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3 (AAMAS'04) New York City, New York, USA July 19-July 23 ISBN: 0-7695-2092-8
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.
Citation:
Kagan Tumer, Adrian K. Agogino, "Time-Extended Policies in Multi-Agent Reinforcement Learning," aamas, vol. 3, pp.1338-1339, Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3 (AAMAS'04), 2004 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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