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  1. ; ; (, Genetic and evolutionary computation)
    null (Ed.)
    Long term robotic deployments are well described by sparse fitness functions, which are hard to learn from and adapt to. This work introduces Adaptive Multi-Fitness Learning (A-MFL), which augments the structure of Multi-Fitness Learning (MFL) [9] by injecting new behaviors into the agents as the environment changes. A-MFL not only improves system performance in dynamic environments, but also avoids undesirable, unforeseen side-effects of new behaviors by localizing where the new behaviors are used. On a simulated multi-robot problem, A-MFL provides up to 90% improvement over MFL, and 100% over a one-step evolutionary approach. 
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