Environment Shift Games: Are Multiple Agents the Solution, and not the Problem, to Non-Stationarity?

Mey, Alexander, and Frans A. Oliehoek. “Environment Shift Games: Are Multiple Agents the Solution, and not the Problem, to Non-Stationarity?.” In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems , pp. 23-27. 2021.
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Machine learning and artificial intelligence models that interact with and in an environment will unavoidably have impact on this environment and change it. This is often a problem as many methods do not anticipate such a change in the environment and thus may start acting sub-optimally. Although efforts are made to deal with this problem, we believe that a lot of potential is unused. Driven by the recent success of predictive machine learning, we believe that in many scenarios one can predict when and how a change in the environment will occur. In this paper we introduce a blueprint that intimately connects this idea to the multiagent setting, showing that the multiagent community has a pivotal role to play in addressing the challenging problem of changing environments.

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