Multi-agent behavioral control system using deep reinforcement learning

© 2019 Elsevier B.V. Deep reinforcement learning (DRL) has emerged as the dominant approach to achieving successive advancements in the creation of human-wise agents. By leveraging neural networks as decision-making controllers, DRL supplements traditional reinforcement methods to address the curse of dimensionality in complicated tasks. However, agents in complicated environments are likely to get stuck in sub-optimal solutions. In such cases, the agent inadvertently turns into a “zombie” owing to its short-term vision and harmful behaviors. In this study, we use human learning strategies to adjust agent behaviors in high-dimensional environments. Therefore, the agent behaves predictably and succeeds in attaining its designated goal. In summary, the contribution of this study is two-fold. First, we introduce a lightweight workflow that enables a nonexpert to preserve a certain level of safety in AI systems. Specifically, the workflow involves a novel concept of a target map and a multi-agent behavioral control system named Multi-Policy Control System (MPCS). MPCS successfully controls agent behaviors in real time without involving the burden of human feedback. Second, we develop a multi-agent game named Tank Battle that provides a configurable environment to examine agent behaviors and human-agent interactions in DRL. Finally, simulation results show that agents guided by MPCS outperform agents that do not use MPCS with respect to the mean of total rewards and human-like behaviors in complicated environments such as Seaquest and Tank Battle.