Recent successes of Reinforcement Learning (RL) allow an agent to learn policies that surpass human experts but suffers from being time-hungry and data-hungry. By contrast, human learning is significantly faster because prior and general knowledge and multiple information resources are utilized. In this paper, we propose a Planner-Actor-Critic architecture for huMAN-centered planning and learning (PACMAN), where an agent uses its prior, high-level, deterministic symbolic knowledge to plan for goal-directed actions, and also integrates the Actor-Critic algorithm of RL to fine-tune its behavior towards both environmental rewards and human feedback. This work is the first unified framework where knowledge-based planning, RL, and human teaching jointly contribute to the policy learning of an agent. Our experiments demonstrate that PACMAN 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.
@article{arxiv.1909.09209,
title = {A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming},
author = {Daoming Lyu and Fangkai Yang and Bo Liu and Steven Gustafson},
journal= {arXiv preprint arXiv:1909.09209},
year = {2019}
}
Comments
In Proceedings ICLP 2019, arXiv:1909.07646. arXiv admin note: significant text overlap with arXiv:1906.07268