## AbstractA possible approach to control high-dimensional problems, including those in robotics, is to design algorithms that can take advantage of the modularity of hierarchical reinforcement learning methods, the speed of value-function-based reinforcement learning algorithms, and the efficacy of policy gradient reinforcement learning methods in dealing with continuous state and action problems. In this work, we define a family of hierarchical reinforcement learning algorithms in which value-function-based reinforcement learning methods are used to control high-level subtasks, which usually involve smaller and more manageable state and finite action spaces, and policy gradient reinforcement learning controllers are used for low-level subtasks, which usually have continuous state and/or action spaces. We call this family of algorithms hierarchical hybrid reinforcement learning.
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