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ATAL
2004
Springer

Time-Extended Policies in Multi-Agent Reinforcement Learning

9 years 22 days ago
Time-Extended Policies in Multi-Agent Reinforcement Learning
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.
Kagan Tumer, Adrian K. Agogino
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where ATAL
Authors Kagan Tumer, Adrian K. Agogino
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