Dual Policy Iteration

Recently, a novel class of Approximate Policy Iteration (API) algorithms have demonstrated impressive practical performance (e. g. , ExIt from [1], AlphaGo-Zero from [2]). This new family of algorithms maintains, and alternately optimizes, two policies: a fast, reactive policy (e. g. , a deep neural network) deployed at test time, and a slow, non-reactive policy (e. g. , Tree Search), that can plan multiple steps ahead. The reactive policy is updated under supervision from the non-reactive policy, while the non-reactive policy is improved with guidance from the reactive policy. In this work we study this Dual Policy Iteration (DPI) strategy in an alternating optimization framework and provide a convergence analysis that extends existing API theory. We also develop a special instance of this framework which reduces the update of non-reactive policies to model-based optimal control using learned local models, and provides a theoretically sound way of unifying model-free and model-based RL approaches with unknown dynamics. We demonstrate the efficacy of our approach on various continuous control Markov Decision Processes.

Authors

• Wen Sun
• Geoffrey Gordon
• Byron Boots
• J. Bagnell

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