Using opponent models to train inexperienced synthetic agents in social environments
Chairi Kiourt, Dimitris Kalles · 2016
This paper investigates the learning progress of inexperienced agents in competitive game playing social environments. We aim to determine the effect of a knowledgeable opponent on a novice learner. For that purpose, we used synthetic agents whose playing behaviors were developed through diverse reinforcement learning set-ups, such as exploitation-vs-exploration trade-off, learning backup and speed of learning, as opponents, and a self-trained agent. The paper concludes by highlighting the effect of diverse knowledgeable synthetic agents in the learning trajectory of an inexperienced agent in competitive multiagent environments.