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 …
Crossing the reality gap: A survey on sim-to-real transferability of robot controllers in reinforcement learning
The growing demand for robots able to act autonomously in complex scenarios has widely
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …
Imagination-augmented agents for deep reinforcement learning
Abstract We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep
reinforcement learning combining model-free and model-based aspects. In contrast to most …
reinforcement learning combining model-free and model-based aspects. In contrast to most …
A review of reinforcement learning for autonomous building energy management
The area of building energy management has received a significant amount of interest in
recent years. This area is concerned with combining advancements in sensor technologies …
recent years. This area is concerned with combining advancements in sensor technologies …
Reinforcement learning based energy management systems and hydrogen refuelling stations for fuel cell electric vehicles: An overview
This paper examines the current state of the art of hydrogen refuelling stations-based
production and storage systems for fuel cell hybrid electric vehicles (FCHEV). Nowadays …
production and storage systems for fuel cell hybrid electric vehicles (FCHEV). Nowadays …
Prioritized memory access explains planning and hippocampal replay
To make decisions, animals must evaluate candidate choices by accessing memories of
relevant experiences. Yet little is known about which experiences are considered or ignored …
relevant experiences. Yet little is known about which experiences are considered or ignored …
Deep dyna-q: Integrating planning for task-completion dialogue policy learning
Training a task-completion dialogue agent via reinforcement learning (RL) is costly because
it requires many interactions with real users. One common alternative is to use a user …
it requires many interactions with real users. One common alternative is to use a user …
Reinforcement learning: An introduction
RS Sutton - A Bradford Book, 2018 - books.google.com
The significantly expanded and updated new edition of a widely used text on reinforcement
learning, one of the most active research areas in artificial intelligence. Reinforcement …
learning, one of the most active research areas in artificial intelligence. Reinforcement …
Reinforcement learning: A survey
This paper surveys the field of reinforcement learning from a computer-science perspective.
It is written to be accessible to researchers familiar with machine learning. Both the historical …
It is written to be accessible to researchers familiar with machine learning. Both the historical …
[書籍][B] Reinforcement learning for robots using neural networks
LJ Lin - 1992 - search.proquest.com
Reinforcement learning agents are adaptive, reactive, and self-supervised. The aim of this
dissertation is to extend the state of the art of reinforcement learning and enable its …
dissertation is to extend the state of the art of reinforcement learning and enable its …