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Comprehensive state-action exploration is essential for reinforcement learning (RL) algorithms. It enables them to find optimal solutions and avoid premature convergence. In value-based RL, optimistic initialization of the value function ensures sufficient exploration for finding the optimal solution. Optimistic values lead to curiosity-driven exploration enabling visitation of under-explored regions. However, optimistic initialization has limitations in stochastic and non-stationary environments due to its inability to explore ''infinitely-often''. To address this limitation, we propose a novel exploration strategy for value-based RL, denoted COIN, based on recurring optimistic initialization. By injecting a continual exploration bonus, we overcome the shortcoming of optimistic initialization (sensitivity to environment noise). We provide a rigorous theoretical comparison of COIN versus existing popular exploration strategies and prove it provides a unique set of attributes (coverage, infinite-often, no visitation tracking, and curiosity). We demonstrate the superiority of COIN over popular existing strategies on a designed toy domain as well as present results on common benchmark tasks. We observe that COIN outperforms existing exploration strategies in four out of six benchmark tasks while performing on par with the best baseline on the other two tasks. 

The year 2020 saw the covid-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world have been faced with the challenge of protecting public health while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies. However, to date, even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) and Bayesian inference can be used to optimize mitigation policies that minimize economic impact without overwhelming hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community; (2) an RLbased methodology for optimizing fine-grained mitigation policies within this simulator; and (3) a Hidden Markov Model for predicting infected individuals based on partial observations regarding test results, presence of symptoms, and past physical contacts. This article is part of the special track on AI and COVID-19.