Multi-Agent Reinforcement Learning is becoming increasingly more important in times of autonomous driving and other smart industrial applications. Simultaneously a promising new approach to Reinforcement Learning arises using the inherent properties of quantum mechanics, reducing the trainable parameters of a model significantly. However, gradient-based Multi-Agent Quantum Reinforcement Learning methods often have to struggle with barren plateaus, holding them back from matching the performance of classical approaches. We build upon a existing approach for gradient free Quantum Reinforcement Learning and propose tree approaches with Variational Quantum Circuits for Multi-Agent Reinforcement Learning using evolutionary optimization. We evaluate our approach in the Coin Game environment and compare them to classical approaches. We showed that our Variational Quantum Circuit approaches perform significantly better compared to a neural network with a similar amount of trainable parameters. Compared to the larger neural network, our approaches archive similar results using $97.88\%$ less parameters.

翻译：随着自动驾驶及其他智能工业应用的兴起，多智能体强化学习正变得日益重要。与此同时，利用量子力学固有特性的强化学习新方法崭露头角，能够显著减少模型的可训练参数。然而，基于梯度的多智能体量子强化学习方法常受困于贫瘠高原效应，难以达到经典方法的性能水平。本文基于现有的无梯度量子强化学习方法，提出采用变分量子电路与进化优化的三种多智能体强化学习方案。我们在Coin Game环境中评估了所提方法，并与经典方法进行了对比。结果表明，我们的变分量子电路方法在可训练参数数量相近的情况下，性能显著优于神经网络。与规模更大的神经网络相比，所提方法在参数减少97.88%的情况下仍能达到相近的结果。