Deep reinforcement learning: Algorithm, applications, and ultra-low-power implementation
In order to overcome the limitation of traditional reinforcement learning techniques on the
restricted dimensionality of state and action spaces, the recent breakthroughs of deep …
restricted dimensionality of state and action spaces, the recent breakthroughs of deep …
Deep reinforcement learning: Framework, applications, and embedded implementations
The recent breakthroughs of deep reinforcement learning (DRL) technique in Alpha Go and
playing Atari have set a good example in handling large state and actions spaces of …
playing Atari have set a good example in handling large state and actions spaces of …
Energy efficient task scheduling based on deep reinforcement learning in cloud environment: A specialized review
H Hou, SNA Jawaddi, A Ismail - Future Generation Computer Systems, 2023 - Elsevier
The expanding scale of cloud data centers and the diversification of user services have led
to an increase in energy consumption and greenhouse gas emissions, resulting in long-term …
to an increase in energy consumption and greenhouse gas emissions, resulting in long-term …
A deep reinforcement learning approach to resource management in hybrid clouds harnessing renewable energy and task scheduling
The use of cloud computing for delivering application services over the Internet has gained
rapid traction. Since the beginning of the COVID-19 global pandemic, the work from home …
rapid traction. Since the beginning of the COVID-19 global pandemic, the work from home …
Deep-reinforcement-learning-based sustainable energy distribution for wireless communication
G Muhammad, MS Hossain - IEEE Wireless Communications, 2021 - ieeexplore.ieee.org
Many countries and organizations have proposed smart city projects to address the
exponential growth of the population by promoting and developing a new paradigm for …
exponential growth of the population by promoting and developing a new paradigm for …
A Multi-Agent Deep Constrained Q-Learning Method for Smart Building Energy Management Under Uncertainties
Data-driven energy management with flexible appliances in smart buildings is a key towards
power system operational intelligence. However, the low efficiency of existing deep …
power system operational intelligence. However, the low efficiency of existing deep …
Distributed deep reinforcement learning for intelligent load scheduling in residential smart grids
The power consumption of households has been constantly growing over the years. To cope
with this growth, intelligent management of the consumption profile of the households is …
with this growth, intelligent management of the consumption profile of the households is …
Service management and energy scheduling toward low-carbon edge computing
Edge computing has become an alternative low-latency provision of cloud computing thanks
to its close-proximity to the users, and the geo-distribution nature of edge servers enables …
to its close-proximity to the users, and the geo-distribution nature of edge servers enables …
Policy-approximation based deep reinforcement learning techniques: an overview
Abstract Until recently, Deep Reinforcement Learning was restricted to innovations in games
like Atari, Dota2. Despite surpassing the benchmarks established by their human …
like Atari, Dota2. Despite surpassing the benchmarks established by their human …
A deep reinforcement learning-based task scheduling algorithm for energy efficiency in data centers
Cloud data centers provide end-users with a wide range of application scenarios, including
scientific computing, smart grids, etc. The number and size of data centers have rapidly …
scientific computing, smart grids, etc. The number and size of data centers have rapidly …