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 …
Meta-learning based spatial-temporal graph attention network for traffic signal control
Traffic signal control is of great importance to the urban transportation systems and public
travel, yet it becomes challenging because of two essential factors. First, spatial–temporal …
travel, yet it becomes challenging because of two essential factors. First, spatial–temporal …