Lifelong Hyper-Policy Optimization with Multiple Importance Sampling Regularization

Pierre Liotet, Francesco Vidaich, Alberto Maria Metelli, and Marcello Restelli

The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2022. (To appear)

Acceptance rate: 1349/9020 (15.0%)
CORE 2021: A*   GGS 2021: A++

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.