Lifelong Hyper-Policy Optimization with Multiple Importance Sampling Regularization

• URL: http://arxiv.org/abs/2112.06625v1
• Date: Mon, 13 Dec 2021 13:09:49 GMT
• Title: Lifelong Hyper-Policy Optimization with Multiple Importance Sampling Regularization
• Authors: Pierre Liotet, Francesco Vidaich, Alberto Maria Metelli, Marcello Restelli
• Abstract summary: We propose an approach which learns a hyper-policy, whose input is time, that outputs the parameters of the policy to be queried at that time. This hyper-policy is trained to maximize the estimated future performance, efficiently reusing past data by means of importance sampling. We empirically validate our approach, in comparison with state-of-the-art algorithms, on realistic environments.
• Score: 40.17392342387002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 outputs the parameters of the policy to be queried at that time. This hyper-policy is trained to maximize the estimated future performance, efficiently reusing past data by means of importance sampling, at the cost of introducing a controlled bias. We combine the future performance estimate with the past performance to mitigate catastrophic forgetting. To avoid overfitting the collected data, we derive a differentiable variance bound that we embed as a penalization term. Finally, we empirically validate our approach, in comparison with state-of-the-art algorithms, on realistic environments, including water resource management and trading.

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