OMA-QMIX: Exploring Optimal Multi-Agent Reinforcement Learning Framework in Multi-Action Spaces

In the real world, many tasks involve multiple agents, such as swarm robotics, drone swarm control, and autonomous vehicle coordination, all of which can be modeled as Multi-Agent Reinforcement Learning (MARL) tasks. Several methods, such as QMIX, have been proposed to address credit assignment problems and learn cooperative strategies in MARL. The latest variant of the state-of-the-art MARL algorithm QMIX aims to relax QMIX's monotonicity constraint to improve SMAC's performance. However, these methods still lack thorough exploration, and agents struggle to identify states worthy of exploration, making it challenging to coordinate exploration efforts on these states and leading to suboptimal policies. In this paper, we propose an exploration-oriented Multi-Agent Reinforcement Learning framework called OMA-QMIX, where agents set independent entropy temperatures for each agent during exploration, selecting targets from multiple projected state spaces to explore action spaces and approximate total state values. Additionally, we utilize Transformers to capture relationships and information from other agents for exploitation, fostering coordination with the rest of the agents. Experimental results demonstrate that OMA-QMIX significantly outperforms state-of-the-art algorithms in the StarCraft Multi-Agent Challenge. Particularly on SMAC tasks, OMAQMIX achieves a success rate of 100% on almost all Hard Maps and Super Hard Maps.