The success or failure of any learning algorithm is partially due to the exploration strategy it exerts. However, most exploration strategies assume that the environment is star tionary and non-strategic. This work investigates how to design exploration strategies in non-stationary and adversarial environments. Our experimental setting uses a two agents strategic interaction scenario, where the opponent switches between different behavioral patterns. The agent's objective is to learn a model of the opponent's strategy to act optimally, despite non-determinism and stochasticity. Our contribution is twofold. First, we present drift exploration as a strategy for switch detection. Second, we propose a new algorithm called R-MAX# that reasons and acts in terms of two objectives: 1) to maximize utilities in the short term while learning and 2) eventually explore implicitly looking for opponent behavioral changes. We provide theoretical results showing that R-MAX# is guaranteed to detect the opponent's switch and learn a new model in terms of finite sample complexity.

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International Joint Conference on Autonomous Agents and Multiagent Systems
Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

Hernandez-Leal, P, Zhan, Y, Taylor, M.E, Munoz de Cote, E, & Sucar, L.E. (2017). An exploration strategy facing non-stationary agents (JAAMAS paper). In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS (pp. 922–923).