Robust Subtask Learning for Compositional Generalization

• URL: http://arxiv.org/abs/2302.02984v2
• Date: Thu, 8 Jun 2023 17:31:49 GMT
• Title: Robust Subtask Learning for Compositional Generalization
• Authors: Kishor Jothimurugan, Steve Hsu, Osbert Bastani and Rajeev Alur
• Abstract summary: We focus on the problem of training subtask policies in a way that they can be used to perform any task. We aim to maximize the worst-case performance over all tasks as opposed to the average-case performance.
• Score: 20.54144051436337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compositional reinforcement learning is a promising approach for training policies to perform complex long-horizon tasks. Typically, a high-level task is decomposed into a sequence of subtasks and a separate policy is trained to perform each subtask. In this paper, we focus on the problem of training subtask policies in a way that they can be used to perform any task; here, a task is given by a sequence of subtasks. We aim to maximize the worst-case performance over all tasks as opposed to the average-case performance. We formulate the problem as a two agent zero-sum game in which the adversary picks the sequence of subtasks. We propose two RL algorithms to solve this game: one is an adaptation of existing multi-agent RL algorithms to our setting and the other is an asynchronous version which enables parallel training of subtask policies. We evaluate our approach on two multi-task environments with continuous states and actions and demonstrate that our algorithms outperform state-of-the-art baselines.

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