Social Learning Methods in Board Games

TL;DR

This paper explores social learning in training game-playing agents, demonstrating that social training leads to more robust and experienced agents compared to traditional self-play methods, especially in complex games.

Contribution

It introduces social learning methods for training game agents and shows their advantages over self-play in producing more robust and diverse strategies.

Findings

Social learning results in more robust agents.

Socially trained agents outperform self-play agents.

Larger populations in Swiss tournaments yield better agents.

Abstract

This paper discusses the effects of social learning in training of game playing agents. The training of agents in a social context instead of a self-play environment is investigated. Agents that use the reinforcement learning algorithms are trained in social settings. This mimics the way in which players of board games such as scrabble and chess mentor each other in their clubs. A Round Robin tournament and a modified Swiss tournament setting are used for the training. The agents trained using social settings are compared to self play agents and results indicate that more robust agents emerge from the social training setting. Higher state space games can benefit from such settings as diverse set of agents will have multiple strategies that increase the chances of obtaining more experienced players at the end of training. The Social Learning trained agents exhibit better playing experience than self play agents. The modified Swiss playing style spawns a larger number of better playing agents as the population size increases.