Two-stage reward allocation with decay for multi-agent coordinated behavior for sequential cooperative task by using deep reinforcement learning

Abstract

We propose a two-stage reward allocation method with decay using an extension of replay memory to adapt this rewarding method for deep reinforcement learning (DRL), to generate coordinated behaviors for tasks that can be completed by executing a few subtasks sequentially by heterogeneous agents. An independent learner in cooperative multi-agent systems needs to learn its policies for effective execution of its own responsible subtask, as well as for coordinated behaviors under a certain coordination structure. Although the reward scheme is an issue for DRL, it is difficult to design it to learn both policies. Our proposed method attempts to generate these different behaviors in multi-agent DRL by dividing the timing of rewards into two stages and varying the ratio between them over time. By introducing the coordinated delivery and execution problem with an expiration time, where a task can be executed sequentially by two heterogeneous agents, we experimentally analyze the effect of using various ratios of the reward division in the two-stage allocations on the generated behaviors. The results demonstrate that the proposed method could improve the overall performance relative to those with the conventional one-time or fixed reward and can establish robust coordinated behavior.