  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.
