Adaptive Trust Region Policy Optimization: Global Convergence and Faster Rates for Regularized MDPs

Lior Shani, Yonathan Efroni, Shie Mannor
<span title="2020-04-03">2020</span> <i title="Association for the Advancement of Artificial Intelligence (AAAI)"> <a target="_blank" rel="noopener" href="" style="color: black;">PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE</a> </i> &nbsp;
Trust region policy optimization (TRPO) is a popular and empirically successful policy search algorithm in Reinforcement Learning (RL) in which a surrogate problem, that restricts consecutive policies to be 'close' to one another, is iteratively solved. Nevertheless, TRPO has been considered a heuristic algorithm inspired by Conservative Policy Iteration (CPI). We show that the adaptive scaling mechanism used in TRPO is in fact the natural "RL version" of traditional trust-region methods from
more &raquo; ... nvex analysis. We first analyze TRPO in the planning setting, in which we have access to the model and the entire state space. Then, we consider sample-based TRPO and establish Õ(1/√N) convergence rate to the global optimum. Importantly, the adaptive scaling mechanism allows us to analyze TRPO in regularized MDPs for which we prove fast rates of Õ(1/N), much like results in convex optimization. This is the first result in RL of better rates when regularizing the instantaneous cost or reward.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1609/aaai.v34i04.6021</a> <a target="_blank" rel="external noopener" href="">fatcat:4b6bmrimubejze4a6abanoz2f4</a> </span>
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