| Autor: |
Hernandez-Leal, Pablo, Zhan, Yusen, Taylor, Matthew, Munoz de Cote, Enrique, Sucar, Enrique |
| Přispěvatelé: |
Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands |
| Jazyk: |
angličtina |
| Rok vydání: |
2017 |
| Předmět: |
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| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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