Abstract

Straightforward reinforcement learning for multi-agent co-learning settings often results in poor outcomes. Meta-learning processes beyond straightforward reinforcement learning may be necessary to achieve good (or optimal) outcomes. Algorithmic processes of meta-learning, or "manipulation", will be described, which is a cognitively realistic and effective means for learning cooperation. We will discuss various "manipulation" routines that address the issue of improving multi-agent co-learning. We hope to develop better adaptive means of multi-agent cooperation, without requiring a priori knowledge, and advance multi-agent co-learning beyond existing theories and techniques