Francesco Vidaich

Lifelong Hyper-Policy Optimization with Multiple Importance Sampling Regularization

Learning in a lifelong setting, where the dynamics continually evolve, is a hard challenge for current reinforcement learning algorithms. Yet this would be a much needed feature for practical applications. In this paper, we propose an approach which learns a hyper-policy, whose input is time, that…