Collaboration is a key challenge in distributed multi-agent reinforcement
learning (MARL) environments. Learning frameworks for these decentralized
systems must weigh the benefits of explicit player coordination against the
communication overhead and computational cost of sharing local observations and
environmental data. Quantum computing has sparked a potential synergy between
quantum entanglement and cooperation in multi-agent environments, which could
enable more efficient distributed collaboration with minimal information
sharing. This relationship is largely unexplored, however, as current
state-of-the-art quantum MARL (QMARL) implementations rely on classical
information sharing rather than entanglement over a quantum channel as a
coordination medium. In contrast, in this paper, a novel framework dubbed
entangled QMARL (eQMARL) is proposed. The proposed eQMARL is a distributed
actor-critic framework that facilitates cooperation over a quantum channel and
eliminates local observation sharing via a quantum entangled split critic.
Introducing a quantum critic uniquely spread across the agents allows coupling
of local observation encoders through entangled input qubits over a quantum
channel, which requires no explicit sharing of local observations and reduces
classical communication overhead. Further, agent policies are tuned through
joint observation-value function estimation via joint quantum measurements,
thereby reducing the centralized computational burden. Experimental results
show that eQMARL with ${\Psi}^{+}$ entanglement converges to a cooperative
strategy up to $17.8\%$ faster and with a higher overall score compared to
split classical and fully centralized classical and quantum baselines. The
results also show that eQMARL achieves this performance with a constant factor
of $25$-times fewer centralized parameters compared to the split classical
baseline.

提出了一种名为 eQMARL 的新型框架，通过量子通道促进协作，并通过量子纠缠的分裂评论家消除本地观察共享，实验结果表明，eQMARL 相较于传统的分裂和完全中心化的经典和量子基线，能够在更短的时间内收敛到合作策略，且拥有更高的整体分数，与传统的分裂经典基线相比，eQMARL 只需要少于 25 倍的中心化参数。