Learning to Coordinate via Quantum Entanglement in Multi-Agent Reinforcement Learning
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A new framework for multi-agent reinforcement learning (MARL) leverages shared quantum entanglement to enhance coordination without communication, surpassing previous methods that relied on shared randomness. This approach introduces a differentiable policy parameterization and a novel architecture separating quantum coordination from local decision-making. Results show strategies achieving quantum advantage in both single-round cooperative games and decentralized partially observable Markov decision processes (Dec-POMDPs), suggesting significant advancements in MARL performance.
New Framework Utilizes Quantum Entanglement for Coordination in Multi-Agent Reinforcement Learning
A groundbreaking approach in multi-agent reinforcement learning (MARL) leverages quantum entanglement to enhance coordination among agents without direct communication. This marks the first framework that allows MARL agents to exploit shared quantum entanglement for developing more effective correlated policies than those achievable through traditional shared randomness.
Research indicates that for certain cooperative games that prohibit communication, strategies utilizing shared quantum entanglement can outperform those based solely on shared randomness. The framework integrates a novel differentiable policy parameterization and an architecture designed to separate joint policies into a quantum coordinator and decentralized local actors.
The new framework enables optimization over quantum measurements, allowing agents to learn strategies that achieve quantum advantage in single-round games. The researchers demonstrated this capability using black box oracles to illustrate how agents can learn effective strategies without pre-existing communication protocols.
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📰 Original Source: https://arxiv.org/abs/2602.08965v1
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