Goal Recognition as Reinforcement Learning

2022-02-13 · Leonardo Rosa Amado, Reuth Mirsky, Felipe Meneguzzi

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.

📄 PDF Abstract BibTeX arXiv:2202.06356

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leonardoamado/gr_from_demonstration 공식 구현

Tasks

Q-Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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