An Evolutionary Framework for Connect-4 as Test-Bed for Comparison of Advanced Minimax, Q-Learning and MCTS

TL;DR

This paper introduces an evolutionary framework to systematically compare advanced versions of RL, Minimax, and MCTS algorithms in Connect-4, providing insights into their relative performance and decision-making efficiency.

Contribution

It develops a novel evaluation method called the Evolutionary Tournament and compares advanced algorithms against standard ones in Connect-4.

Findings

MCTS achieves the highest win percentage.

Minimax outperforms standard versions.

Q-Learning is the fastest decision-maker.

Abstract

A major challenge in decision making domains with large state spaces is to effectively select actions which maximize utility. In recent years, approaches such as reinforcement learning (RL) and search algorithms have been successful to tackle this issue, despite their differences. RL defines a learning framework that an agent explores and interacts with. Search algorithms provide a formalism to search for a solution. However, it is often difficult to evaluate the performances of such approaches in a practical way. Motivated by this problem, we focus on one game domain, i.e., Connect-4, and develop a novel evolutionary framework to evaluate three classes of algorithms: RL, Minimax and Monte Carlo tree search (MCTS). The contribution of this paper is threefold: i) we implement advanced versions of these algorithms and provide a systematic comparison with their standard counterpart, ii) we develop a novel evaluation framework, which we call the Evolutionary Tournament, and iii) we conduct an extensive evaluation of the relative performance of each algorithm to compare our findings. We evaluate different metrics and show that MCTS achieves the best results in terms of win percentage, whereas Minimax and Q-Learning are ranked in second and third place, respectively, although the latter is shown to be the fastest to make a decision.