Despite significant advances in the field of deep Reinforcement Learning (RL), today's
algorithms still fail to learn human-level policies consistently over a set of diverse tasks such …

Deep Q-Learning (DQL), a family of temporal difference algorithms for control, employs three
techniques collectively known as thedeadly triad'in reinforcement learning: bootstrapping, off …

A deep Q network (DQN)(Mnih et al.,) is an extension of Q learning, which is a typical deep
reinforcement learning method. In DQN, a Q function expresses all action values under all …

Battery swapping stations (BSSs) are ideal candidates for fast frequency regulation services
(FFRS) due to their large battery stock capacity. In addition, BSSs can precharge batteries …

With the increasing complexity of modern power system, conventional dynamic load
modeling with ZIP and induction motors (ZIP+ IM) is no longer adequate to address the …

Deep reinforcement learning (DRL) algorithms have successfully been demonstrated on a
range of challenging decision making and control tasks. One dominant component of recent …

Accurate value estimates are important for off-policy reinforcement learning. Algorithms
based on temporal difference learning typically are prone to an over-or underestimation bias …

State-of-the art deep reinforcement learning has enabled autonomous agents to learn
complex strategies from scratch on many problems including continuous control tasks. Deep …

This work aims at constructing a bridge between robust control theory and reinforcement
learning. Although, reinforcement learning has shown admirable results in complex control …

Artificial intelligence (AI) techniques in power grid control and energy management in
building automation require both deep Q-networks (DQNs) and deep deterministic policy …