Prioritized Tasks Mining for Multi-Task Cooperative Multi-Agent Reinforcement Learning

Yang Yu, Qiyue Yin, Junge Zhang, Kaiqi Huang

Abstract: Multi-task learning improves data efficiency in cooperative multi-agent reinforcement learning, since agents can learn multiple related tasks simultaneously and the cooperation knowledge in a task can be utilized by others. However, existing methods mainly learn multiple cooperation tasks uniformly, regardless of their complexity and significance. In this paper, we propose a new framework called Prioritized Tasks Mining (PTM) for multi-task cooperation problems, which helps agents to identify and mine higher priority cooperation tasks, so as to learn more effective coordinated strategies for multiple cooperation tasks. Specially, agents will use the hindsight during training to identify the priority of different tasks, and make an exploration and exploitation in higher priority cooperative tasks to mine more sophisticated coordinated strategies. We evaluate PTM in challenging multi-task StarCraft micromanagement games with different scales, and results demonstrate that our method consistently outperforms all strong baselines.

External IDs:dblp:conf/atal/YuYZH23