A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution
AH Ganesh, B Xu - Renewable and Sustainable Energy Reviews, 2022 - Elsevier
The impact of internal combustion engine-powered automobiles on climate change due to
emissions and the depletion of fossil fuels has contributed to the progress of electrified …
emissions and the depletion of fossil fuels has contributed to the progress of electrified …
A review of cooperative multi-agent deep reinforcement learning
A Oroojlooy, D Hajinezhad - Applied Intelligence, 2023 - Springer
Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …
systems in recent years. The aim of this review article is to provide an overview of recent …
A survey of meta-reinforcement learning
While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …
machine learning, it is held back from more widespread adoption by its often poor data …
Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation
Traffic signal control is an important and challenging real-world problem that has recently
received a large amount of interest from both transportation and computer science …
received a large amount of interest from both transportation and computer science …
[HTML][HTML] Leveraging reinforcement learning for dynamic traffic control: A survey and challenges for field implementation
In recent years, the advancement of artificial intelligence techniques has led to significant
interest in reinforcement learning (RL) within the traffic and transportation community …
interest in reinforcement learning (RL) within the traffic and transportation community …
A survey on traffic signal control methods
Traffic signal control is an important and challenging real-world problem, which aims to
minimize the travel time of vehicles by coordinating their movements at the road …
minimize the travel time of vehicles by coordinating their movements at the road …
Reinforcement learning benchmarks for traffic signal control
We propose a toolkit for developing and comparing reinforcement learning (RL)-based traffic
signal controllers. The toolkit includes implementation of state-of-the-art deep-RL algorithms …
signal controllers. The toolkit includes implementation of state-of-the-art deep-RL algorithms …
Near-optimal model-free reinforcement learning in non-stationary episodic mdps
We consider model-free reinforcement learning (RL) in non-stationary Markov decision
processes. Both the reward functions and the state transition functions are allowed to vary …
processes. Both the reward functions and the state transition functions are allowed to vary …
Hierarchically and cooperatively learning traffic signal control
Deep reinforcement learning (RL) has been applied to traffic signal control recently and
demonstrated superior performance to conventional control methods. However, there are …
demonstrated superior performance to conventional control methods. However, there are …
An interdisciplinary survey on origin-destination flows modeling: Theory and techniques
Origin-destination (OD) flow modeling is an extensively researched subject across multiple
disciplines, such as the investigation of travel demand in transportation and spatial …
disciplines, such as the investigation of travel demand in transportation and spatial …