Quantum algorithms for reinforcement learning with a generative model

Daochen Wang · Aarthi Sundaram · Robin Kothari · Ashish Kapoor · Martin Roetteler

Keywords: [ Non-convex Optimization ] [ Algorithms -> Collaborative Filtering; Applications -> Information Retrieval; Applications ] [ Matrix and Tensor Factorization; ] [ RL, Decisions and Control Theory ]

[ Abstract ]
[ Slides [ Paper ] [ Visit Poster at Spot B6 in Virtual World ]
Thu 22 Jul 9 a.m. PDT — 11 a.m. PDT
Spotlight presentation: Learning Theory 14
Thu 22 Jul 6 a.m. PDT — 7 a.m. PDT

Abstract: Reinforcement learning studies how an agent should interact with an environment to maximize its cumulative reward. A standard way to study this question abstractly is to ask how many samples an agent needs from the environment to learn an optimal policy for a $\gamma$-discounted Markov decision process (MDP). For such an MDP, we design quantum algorithms that approximate an optimal policy ($\pi^*$), the optimal value function ($v^*$), and the optimal $Q$-function ($q^*$), assuming the algorithms can access samples from the environment in quantum superposition. This assumption is justified whenever there exists a simulator for the environment; for example, if the environment is a video game or some other program. Our quantum algorithms, inspired by value iteration, achieve quadratic speedups over the best-possible classical sample complexities in the approximation accuracy ($\epsilon$) and two main parameters of the MDP: the effective time horizon ($\frac{1}{1-\gamma}$) and the size of the action space ($A$). Moreover, we show that our quantum algorithm for computing $q^*$ is optimal by proving a matching quantum lower bound.

Chat is not available.