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 …
Deep reinforcement learning in computer vision: a comprehensive survey
Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …
the powerful representation of deep neural networks. Recent works have demonstrated the …
Deep reinforcement learning for de novo drug design
We have devised and implemented a novel computational strategy for de novo design of
molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural …
molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural …
Deep reinforcement learning in medical imaging: A literature review
Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …
learns a sequence of actions that maximizes the expected reward, with the representative …
Q-learning decision transformer: Leveraging dynamic programming for conditional sequence modelling in offline rl
Recent works have shown that tackling offline reinforcement learning (RL) with a conditional
policy produces promising results. The Decision Transformer (DT) combines the conditional …
policy produces promising results. The Decision Transformer (DT) combines the conditional …
Deep reinforcement learning
SE Li - Reinforcement learning for sequential decision and …, 2023 - Springer
Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …
decision strategies. However, in many cases, it is desirable to learn directly from …
Evolution-guided policy gradient in reinforcement learning
Abstract Deep Reinforcement Learning (DRL) algorithms have been successfully applied to
a range of challenging control tasks. However, these methods typically suffer from three core …
a range of challenging control tasks. However, these methods typically suffer from three core …
Longevity-aware energy management for fuel cell hybrid electric bus based on a novel proximal policy optimization deep reinforcement learning framework
R Huang, H He, X Zhao, M Gao - Journal of Power Sources, 2023 - Elsevier
With the prosperity of artificial intelligence and new energy vehicles, energy-saving
technologies for zero-emission fuel cell hybrid electric vehicles through high-efficient deep …
technologies for zero-emission fuel cell hybrid electric vehicles through high-efficient deep …
Multi-step reinforcement learning for model-free predictive energy management of an electrified off-highway vehicle
The energy management system of an electrified vehicle is one of the most important
supervisory control systems which manages the use of on-board energy resources. This …
supervisory control systems which manages the use of on-board energy resources. This …
Politex: Regret bounds for policy iteration using expert prediction
Abstract We present POLITEX (POLicy ITeration with EXpert advice), a variant of policy
iteration where each policy is a Boltzmann distribution over the sum of action-value function …
iteration where each policy is a Boltzmann distribution over the sum of action-value function …