The paper's state-of-the-art review focuses on an in-depth evaluation of smart home energy
management systems which employ reinforcement learning-based methods to integrate …

We consider the problem of learning the best possible policy from a fixed dataset, known as
offline Reinforcement Learning (RL). A common taxonomy of existing offline RL works is …

The average reward criterion is relatively less studied as most existing works in the
Reinforcement Learning literature consider the discounted reward criterion. There are few …

Direct policy optimization in reinforcement learning is usually solved with policy-gradient
algorithms, which optimize policy parameters via stochastic gradient ascent. This paper …

Network slicing (NS) provides customized services to users of the Internet of Things (IoT) by
creating logical virtual networks, and NS combined with multiaccess edge computing (MEC) …

Policy gradient (PG) methods are successful approaches to deal with continuous
reinforcement learning (RL) problems. They learn stochastic parametric (hyper) policies by …

In offline reinforcement learning, the challenge of out-of-distribution (OOD) is pronounced.
To address this, existing methods often constrain the learned policy through policy …

This paper proposes a policy learning algorithm based on the Koopman operator theory and
policy gradient approach, which seeks to approximate an unknown dynamical system and …

Policy gradient method typically suffers high variance, which is further amplified in the multi-
agent setting due to the exponential explosive growth of the joint action space. While value …