Quantum Advantage Actor-Critic for Reinforcement Learning
This work addresses scalability concerns in reinforcement learning for control tasks, but it is incremental as it builds on existing quantum and classical methods.
The authors tackled the scalability problem in reinforcement learning by proposing a quantum Advantage Actor-Critic approach that substitutes classical components with variational quantum circuits, achieving a substantial performance increase in the Cart Pole environment compared to pure classical and quantum variants.
Quantum computing offers efficient encapsulation of high-dimensional states. In this work, we propose a novel quantum reinforcement learning approach that combines the Advantage Actor-Critic algorithm with variational quantum circuits by substituting parts of the classical components. This approach addresses reinforcement learning's scalability concerns while maintaining high performance. We empirically test multiple quantum Advantage Actor-Critic configurations with the well known Cart Pole environment to evaluate our approach in control tasks with continuous state spaces. Our results indicate that the hybrid strategy of using either a quantum actor or quantum critic with classical post-processing yields a substantial performance increase compared to pure classical and pure quantum variants with similar parameter counts. They further reveal the limits of current quantum approaches due to the hardware constraints of noisy intermediate-scale quantum computers, suggesting further research to scale hybrid approaches for larger and more complex control tasks.