Punctuated anytime learning for evolving multi-agent capture strategies

The evolution of a team of heterogeneous agents is challenging. To allow the greatest level of specialization team members must be evolved in separate populations, but finding acceptable partners for evaluation at trial time is difficult. Testing too few partners blinds the GA from recognizing fit solutions while testing too many partners makes the computation time unmanageable. We developed a system based on punctuated anytime learning that periodically tests a number of partner combinations to select a single individual from each population to be used at trial time. We previously tested our method with a two agent box-pushing task. In this work, we show the efficiency of our method by applying it to the predator-prey scenario.