Abstract Deep Reinforcement Learning (DRL) has gained significant adoption in diverse
fields and applications, mainly due to its proficiency in resolving complicated decision …

We study policy gradient (PG) for reinforcement learning in continuous time and space
under the regularized exploratory formulation developed by Wang et al.(2020). We …

Text-based interactive recommendation provides richer user preferences and has
demonstrated advantages over traditional interactive recommender systems. However …

We study reinforcement learning (RL) in the setting of continuous time and space, for an
infinite horizon with a discounted objective and the underlying dynamics driven by a …

Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the
robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level …

We study the continuous-time counterpart of Q-learning for reinforcement learning (RL)
under the entropy-regularized, exploratory diffusion process formulation introduced by Wang …

We present two elegant solutions for modeling continuous-time dynamics, in a novel model-
based reinforcement learning (RL) framework for semi-Markov decision processes (SMDPs) …

We study continuous-time reinforcement learning (RL) for stochastic control in which system
dynamics are governed by jump-diffusion processes. We formulate an entropy-regularized …

Reinforcement learning algorithms typically consider discrete-time dynamics, even though
the underlying systems are often continuous in time. In this paper, we introduce a model …

The choice of the control frequency of a system has a relevant impact on the ability of
reinforcement learning algorithms to learn a highly performing policy. In this paper, we …