Goal Recognition as Reinforcement Learning

• URL: http://arxiv.org/abs/2202.06356v1
• Date: Sun, 13 Feb 2022 16:16:43 GMT
• Title: Goal Recognition as Reinforcement Learning
• Authors: Leonardo Rosa Amado and Reuth Mirsky and Felipe Meneguzzi
• Abstract summary: We develop a framework that combines model-free reinforcement learning and goal recognition. This framework consists of two main stages: Offline learning of policies or utility functions for each potential goal, and online inference. The resulting instantiation achieves state-of-the-art performance against goal recognizers on standard evaluation domains and superior performance in noisy environments.
• Score: 20.651718821998106
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most approaches for goal recognition rely on specifications of the possible dynamics of the actor in the environment when pursuing a goal. These specifications suffer from two key issues. First, encoding these dynamics requires careful design by a domain expert, which is often not robust to noise at recognition time. Second, existing approaches often need costly real-time computations to reason about the likelihood of each potential goal. In this paper, we develop a framework that combines model-free reinforcement learning and goal recognition to alleviate the need for careful, manual domain design, and the need for costly online executions. This framework consists of two main stages: Offline learning of policies or utility functions for each potential goal, and online inference. We provide a first instance of this framework using tabular Q-learning for the learning stage, as well as three measures that can be used to perform the inference stage. The resulting instantiation achieves state-of-the-art performance against goal recognizers on standard evaluation domains and superior performance in noisy environments.

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