Efficient Use of heuristics for accelerating XCS-based Policy Learning in Markov Games

TL;DR

This paper introduces an efficient algorithm combining heuristics, opponent modeling, and rule evolution techniques within XCS to accelerate policy learning in Markov games against adaptive opponents.

Contribution

It presents a novel approach integrating heuristics, opponent modeling with neural networks, Pareto optimality, and accuracy-based traces to improve learning speed and explainability in Markov games.

Findings

Outperforms benchmark algorithms in soccer and thief-and-hunter scenarios.

Enables explainable and generalized action rules.

Speeds up policy learning against non-stationary opponents.

Abstract

In Markov games, playing against non-stationary opponents with learning ability is still challenging for reinforcement learning (RL) agents, because the opponents can evolve their policies concurrently. This increases the complexity of the learning task and slows down the learning speed of the RL agents. This paper proposes efficient use of rough heuristics to speed up policy learning when playing against concurrent learners. Specifically, we propose an algorithm that can efficiently learn explainable and generalized action selection rules by taking advantages of the representation of quantitative heuristics and an opponent model with an eXtended classifier system (XCS) in zero-sum Markov games. A neural network is used to model the opponent from their behaviors and the corresponding policy is inferred for action selection and rule evolution. In cases of multiple heuristic policies, we introduce the concept of Pareto optimality for action selection. Besides, taking advantages of the condition representation and matching mechanism of XCS, the heuristic policies and the opponent model can provide guidance for situations with similar feature representation. Furthermore, we introduce an accuracy-based eligibility trace mechanism to speed up rule evolution, i.e., classifiers that can match the historical traces are reinforced according to their accuracy. We demonstrate the advantages of the proposed algorithm over several benchmark algorithms in a soccer and a thief-and-hunter scenarios.