Concept Accessibility as Basis for Evolutionary Reinforcement Learning of Dots and Boxes

Abstract

The challenge of creating teams of agents, which evolve or learn, to solve complex problems is addressed in the combinatorially complex game of dots and boxes (strings and coins). Previous evolutionary reinforcement learning (ERL) systems approaching this task based on dynamic agent populations have shown some degree of success in game play, however are sensitive to conditions and suffer from unstable agent populations under difficult play and poor development against an easier opponent. A novel technique for preserving stability and allowing balance of specialised and generalised rules in an ERL system is presented, motivated by accessibility of concepts in human cognition, as opposed to natural selection through population survivability common to ERL systems. Reinforcement learning in dynamic teams of mutable agents enables play comparable to hand-crafted artificial players. Performance and stability of development is enhanced when a measure of the frequency of reinforcement is separated from the quality measure of rules