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Proceedings of the 13th annual conference on Genetic and evolutionary computation
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S-Race: a multi-objective racing algorithm
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Lazy paired hyper-parameter tuning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Uncertainty arises in reinforcement learning from various sources, and therefore it is necessary to consider statistics based on several roll-outs for evaluating behavioral policies. We add an adaptive uncertainty handling based on Hoeffding and empirical Bernstein races to the CMA-ES, a variable metric evolution strategy proposed for direct policy search. The uncertainty handling adjusts individually the number of episodes considered for the evaluation of a policy. The performance estimation is kept just accurate enough for a sufficiently good ranking of candidate policies, which is in turn sufficient for the CMA-ES to find better solutions. This increases the learning speed as well as the robustness of the algorithm.