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Recent successes of Reinforcement Learning (RL) allow an agent to learnpolicies that surpass human experts but suffers from being time-hungry anddata-hungry. By contrast, human learning is significantly faster because priorand general knowledge and multiple information resources are utilized. In thispaper, we propose a Planner-Actor-Critic architecture for huMAN-centeredplanning and learning (PACMAN), where an agent uses its prior, high-level,deterministic symbolic knowledge to plan for goal-directed actions, and alsointegrates the Actor-Critic algorithm of RL to fine-tune its behavior towardsboth environmental rewards and human feedback. This work is the first unifiedframework where knowledge-based planning, RL, and human teaching jointlycontribute to the policy learning of an agent. Our experiments demonstrate thatPACMAN leads to a significant jump-start at the early stage of learning,converges rapidly and with small variance, and is robust to inconsistent,infrequent, and misleading feedback.

A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming