Games, by definition, offer the challenge of the presence of an opponent, to which a playing strategy should respond. In finite-timed zero-sum games, the strategy should enable to win the game within a limited playing time. Motivated by robot soccer, in this talk, we will present several approaches towards learning to select team strategies in such finite-timed zero-sum games. We will introduce an adaptive playbook approach with implicit opponent modeling, in which multiple team strategies are represented as variable weighted plays. We will discuss different plays as a function of different game situations and opponents. In conclusion, we will present an MDP-based learning algorithm to reason in particular about current score and game time left. Through extensive simulated empirical studies, we will demonstrate the effectiveness of the learning approach. In addition, the talk will include illustrative examples from robot soccer. The major part of this work is in conjunction with my PhD student Colin McMillen.
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- Learning to Select Team Strategies in Finite-Timed Zero-Sum Games
- Springer Berlin Heidelberg