Low-rank State-action Value-function Approximation
This addresses scalability issues in reinforcement learning for high-dimensional state problems, offering a non-parametric alternative to existing approximation methods.
The paper tackles the curse of dimensionality in value-function estimation by proposing stochastic algorithms for low-rank factorization of the Q(s,a) matrix, resulting in reduced computational and sample complexities compared to classical Q-learning methods.
Value functions are central to Dynamic Programming and Reinforcement Learning but their exact estimation suffers from the curse of dimensionality, challenging the development of practical value-function (VF) estimation algorithms. Several approaches have been proposed to overcome this issue, from non-parametric schemes that aggregate states or actions to parametric approximations of state and action VFs via, e.g., linear estimators or deep neural networks. Relevantly, several high-dimensional state problems can be well-approximated by an intrinsic low-rank structure. Motivated by this and leveraging results from low-rank optimization, this paper proposes different stochastic algorithms to estimate a low-rank factorization of the $Q(s, a)$ matrix. This is a non-parametric alternative to VF approximation that dramatically reduces the computational and sample complexities relative to classical $Q$-learning methods that estimate $Q(s,a)$ separately for each state-action pair.