Uncovering Instabilities in Variational-Quantum Deep Q-Networks

Understand

Deep Reinforcement Learning (RL) has considerably advanced over the past decade.

• At the same time, state-of-the-art RL algorithms require a large computational budget in terms of training time to converge.
• Recent work has started to approach this problem through the lens of quantum computing, which promises theoretical speed-ups for several traditionally hard tasks.
• In this work, we examine a class of hybrid quantum-classical RL algorithms that we collectively refer to as variational quantum deep Q-networks (VQ-DQN).