Mirror Descent Policy Optimization

• URL: http://arxiv.org/abs/2005.09814v5
• Date: Mon, 7 Jun 2021 13:44:15 GMT
• Title: Mirror Descent Policy Optimization
• Authors: Manan Tomar, Lior Shani, Yonathan Efroni, Mohammad Ghavamzadeh
• Abstract summary: We propose an efficient RL algorithm, called em mirror descent policy optimization (MDPO) MDPO iteratively updates the policy by em approximately solving a trust-region problem. We highlight the connections between on-policy MDPO and two popular trust-region RL algorithms: TRPO and PPO, and show that explicitly enforcing the trust-region constraint is in fact em not a necessity for high performance gains in TRPO.
• Score: 41.46894905097985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mirror descent (MD), a well-known first-order method in constrained convex optimization, has recently been shown as an important tool to analyze trust-region algorithms in reinforcement learning (RL). However, there remains a considerable gap between such theoretically analyzed algorithms and the ones used in practice. Inspired by this, we propose an efficient RL algorithm, called {\em mirror descent policy optimization} (MDPO). MDPO iteratively updates the policy by {\em approximately} solving a trust-region problem, whose objective function consists of two terms: a linearization of the standard RL objective and a proximity term that restricts two consecutive policies to be close to each other. Each update performs this approximation by taking multiple gradient steps on this objective function. We derive {\em on-policy} and {\em off-policy} variants of MDPO, while emphasizing important design choices motivated by the existing theory of MD in RL. We highlight the connections between on-policy MDPO and two popular trust-region RL algorithms: TRPO and PPO, and show that explicitly enforcing the trust-region constraint is in fact {\em not} a necessity for high performance gains in TRPO. We then show how the popular soft actor-critic (SAC) algorithm can be derived by slight modifications of off-policy MDPO. Overall, MDPO is derived from the MD principles, offers a unified approach to viewing a number of popular RL algorithms, and performs better than or on-par with TRPO, PPO, and SAC in a number of continuous control tasks. Code is available at \url{https://github.com/manantomar/Mirror-Descent-Policy-Optimization}.

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