Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret

arXiv:2302.10796v2 Announce Type: replace-cross
Abstract: While quantum reinforcement learning (RL) has attracted a surge of attention recently, its theoretical understanding is limited. In particular, it remains elusive how to design provably efficient quantum RL algorithms that can address the exploration-exploitation trade-off. To this end, we propose a novel UCRL-style algorithm that takes advantage of quantum computing for tabular Markov decision processes (MDPs) with $S$ states, $A$ actions, and horizon $H$, and establish an $\mathcal{O}(\mathrm{poly}(S, A, H, \log T))$ worst-case regret …

abstract algorithms arxiv attention case cs.ai cs.lg design exploitation exploration novel off quant-ph quantum reinforcement reinforcement learning replace stat.ml style trade trade-off type understanding while