Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …

Mobile robot navigation is typically regarded as a geometric problem, in which the robot's
objective is to perceive the geometry of the environment in order to plan collision-free paths …

Power system emergency control is generally regarded as the last safety net for grid security
and resiliency. Existing emergency control schemes are usually designed offline based on …

The history of learning for control has been an exciting back and forth between two broad
classes of algorithms: planning and reinforcement learning. Planning algorithms effectively …

Natural language provides an accessible and expressive interface to specify long-term tasks
for robotic agents. However, non-experts are likely to specify such tasks with high-level …