Entangled QMARL agents approach the Tsirelson bound of 0.854 in CHSH while unentangled versions match classical baselines, and hybrid quantum-classical setups outperform both in CoopNav.
The Complexity of Decentralized Control of Markov Decision Processes
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abstract
Planning for distributed agents with partial state information is considered from a decision- theoretic perspective. We describe generalizations of both the MDP and POMDP models that allow for decentralized control. For even a small number of agents, the finite-horizon problems corresponding to both of our models are complete for nondeterministic exponential time. These complexity results illustrate a fundamental difference between centralized and decentralized control of Markov processes. In contrast to the MDP and POMDP problems, the problems we consider provably do not admit polynomial-time algorithms and most likely require doubly exponential time to solve in the worst case. We have thus provided mathematical evidence corresponding to the intuition that decentralized planning problems cannot easily be reduced to centralized problems and solved exactly using established techniques.
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Quantum Advantage in Multi Agent Reinforcement Learning
Entangled QMARL agents approach the Tsirelson bound of 0.854 in CHSH while unentangled versions match classical baselines, and hybrid quantum-classical setups outperform both in CoopNav.