… reinforcement learning framework for continuoustime dynamical systems without a priori
discretization of time… as the minimization of a continuous-time form of the temporal difference (TD…

… To contrast the robustness of discrete and continuoustime techniques, we evaluate MPETS
and ENODE on more realistic datasets of irregularly sampled and noisy data sequences, …

… We consider reinforcement learning (RL) in continuous time with continuous feature and …
formulation for the feature dynamics that captures learning under exploration, with the resulting …

… is applicable to reinforcement learning systems working in continuous time (or discrete time
with small time steps) for which standard algorithms such as Q-learning are not applicable. …

… ) - extending the domain of applicability to continuous time. This effort was originally motivated
by the desire to apply reinforcement learning methods to problems of adaptive control of …

This exposition discusses continuous-time reinforcement learning (CT-RL) for the control of
affine nonlinear systems. We review four seminal methods that are the centerpieces of the …

… ) are unknown, we can use reinforcement learning to obtain an optimal and robust control …
(15) The above expression can be written as the integral reinforcement learning (IRL) form (16…

… of policy optimization in the context of continuous-time Reinforcement Learning (RL), a branch
… The underlying asset portfolio process is assumed to possess a continuous-time discrete-…

… This paper generalizes the MAXQ method to continuous-time discounted and … reinforcement
learning algorithms: continuous-time discounted reward MAXQ and continuous-time …

… continuous time. Next, we show how spiking neurons can implement a critic, to represent and
learn … Third, we discuss a spiking neuron actor, and how it can represent and learn a policy…