Improving Multi-agent Reinforcement Learning with Imperfect Human Knowledge

Abstract

Multi-agent reinforcement learning has gained great success in many decision-making tasks. However, there are still some challenges such as low efficiency of exploration, significant time consumption, which bring great obstacles for it to be applied in the real world. Incorporating human knowledge into the learning process has been regarded as a promising way to ameliorate these problems. This paper proposes a novel approach to utilize imperfect human knowledge to improve the performance of multi-agent reinforcement learning. We leverage logic rules, which can be seen as a popular form of human knowledge, as part of the action space in reinforcement learning. During the trial-and-error, the value of rules and the original action will be estimated. Logic rules, therefore, can be selected flexibly and efficiently to assist the learning. Moreover, we design a new exploration way, in which rules are preferred to be explored at the early training stage. Finally, we make experimental evaluations and analyses of our approach in challenging StarCraftII micromanagement scenarios. The empirical results show that our approach outperforms the state-of-the-art multi-agent reinforcement learning method, not only in the performance but also in the learning speed.