Social Reinforcement Learning in Game Playing

Abstract

In this work we discuss Social Reinforcement Learning on self-trained agents. We simulate social learning by implementing a tournament on an existing board game that utilizes reinforcement learning for playing and learning. The socially trained agents are compared to self-trained agents and their superior performance is noted. The findings and the infrastructure requirements mandate the development of a Social Reinforcement Learning Multi-Agent-Based Simulation platform.