The increasing scale of distributed energy resources (DERs) in the active distribution
network (ADN) offers valuable opportunities for distribution system operators (DSOs) to …

We present temporally abstract actor-critic (TAAC), a simple but effective off-policy RL
algorithm that incorporates closed-loop temporal abstraction into the actor-critic framework …

This paper presents a study on path planning for 6-DOF free-floating space robotic
manipulators using Deep Deterministic Policy Gradient-based Reinforcement Learning. The …

This work proposes an innovative path-following control method, anchored in deep
reinforcement learning (DRL), for unmanned underwater vehicles (UUVs). This approach is …

The physical world evolves continuously in time. Most prior works on reinforcement learning
cast continuous-time environments into a discrete-time Markov Decision Process (MDP), by …

Diffusion in solids is a slow process that dictates rate-limiting processes in key chemical
reactions. Unlike crystalline solids that offer well-defined diffusion pathways, the lack of …

Reinforcement learning (RL) has demonstrated considerable potential in addressing
intricate control and decision problems such as vehicle tracking control and obstacle …

Deep reinforcement learning has achieved signif-icant results in low-level controlling tasks.
However, for some applications like autonomous driving and drone flying, it is difficult to …

Deep reinforcement learning (RL) is a powerful approach for solving optimal control
problems. However, RL-trained policies often suffer from the action fluctuation problem …

Reinforcement learning (RL) excels in optimizing policies for discrete-time Markov decision
processes (MDP). However, various systems are inherently continuous in time, making …