Abstract Evaluations of Deep Reinforcement Learning (DRL) methods are an integral part of
scientific progress of the field. Beyond designing DRL methods for general intelligence …

In recent years, significant progress has been made in solving challenging problems across
various domains using deep reinforcement learning (RL). Reproducing existing work and …

While deep reinforcement learning (DRL) has enjoyed several recent successes, results
reported in the literature are often difficult to reliably reproduce. Difficulties in reproducibility …

Deep Reinforcement Learning (DRL) is a powerful technique for learning policies for
complex decision-making tasks. In this paper, we provide an overview of DRL, including its …

Many deep reinforcement learning algorithms contain inductive biases that sculpt the
agent's objective and its interface to the environment. These inductive biases can take many …

Deep reinforcement learning (DRL) has achieved impressive success in traffic signal control
systems (TSCS). However, since a key component of many DRL models is the complex …

Abstract Deep Reinforcement Learning (DRL) has emerged as a transformative paradigm in
the field of artificial intelligence (AI), offering unprecedented capabilities in decision-making …

This tutorial aims to provide the audience with a guided introduction to deep reinforcement
learning (DRL) with specially curated application case studies in transportation. The tutorial …

Abstract Deep Reinforcement Learning (DRL) is increasingly applied in cyber–physical
systems for automation tasks. It is important to record the developing trends in DRL's …

Model-based reinforcement learning (RL) is considered to be a promising approach to
reduce the sample complexity that hinders model-free RL. However, the theoretical …