Symbol Acquisition for Task-Level Planning

We consider the problem of how to plan efficiently in low-level, continuous state spaces with temporally abstract actions (or skills), by constructing abstract representations of the problem suitable for task-level planning. The central question this effort poses is which abstract representations are required to express and evaluate plans composed of sequences of skills. We show that classifiers can be used as a symbolic representation system, and that the ability to represent the preconditions and effects of an agent’s skills is both necessary and sufficient for task-level planning. The resulting representations allow a reinforcement learning agent to acquire a symbolic representation appropriate for planning from experience.