Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning

• URL: http://arxiv.org/abs/2011.08345v2
• Date: Tue, 6 Apr 2021 14:06:57 GMT
• Title: Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning
• Authors: Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, Han-Lim Choi
• Abstract summary: We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly. Rather than learning an individual policy for each task, the proposed framework, DISH, distills a hierarchical policy from a set of tasks by representation and reinforcement learning.
• Score: 18.415568038071306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly. Rather than learning an individual policy for each particular task, the proposed framework, DISH, distills a hierarchical policy from a set of tasks by representation and reinforcement learning. The framework is based on the idea of latent variable models that represent high-dimensional observations using low-dimensional latent variables. The resulting policy consists of two levels of hierarchy: (i) a planning module that reasons a sequence of latent intentions that would lead to an optimistic future and (ii) a feedback control policy, shared across the tasks, that executes the inferred intention. Because the planning is performed in low-dimensional latent space, the learned policy can immediately be used to solve or adapt to new tasks without additional training. We demonstrate the proposed framework can learn compact representations (3- and 1-dimensional latent states and commands for a humanoid with 197- and 36-dimensional state features and actions) while solving a small number of imitation tasks, and the resulting policy is directly applicable to other types of tasks, i.e., navigation in cluttered environments. Video: https://youtu.be/HQsQysUWOhg

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