… Deep reinforcement learning (DRL) is a new machine … and prohibitive computation, we design
a Deep Q-network (DQN) for … can improve the energy efficiency in different scenarios. The …

… An aim of this paper is to propose a deep reinforcement learning (DRL)-based approach to
achieve a reduction of energy consumption in UDN. Key ingredient of the proposed scheme …

… The prior approaches for improving data center energy efficiency mostly suffer from high …
based on deep reinforcement learning, named DeepEE, to jointly optimize energy consumption …

… approach, our MAD-DDQN approach is significantly more energy efficient, thereby implying
the MADDPG approach trades energy consumption for improved coverage of ground users. …

… The RL based DQN and LSTM with DPSO is used in this paper for addressing the energy
efficiency issue in cloud where LSTM helps to process the time stamp data with larger size and …

… and minimizing their energy consumption. Toward this end, we propose to leverage …
deep reinforcement learning (DRL) for UAV control and present a novel and highly energyefficient …

… the overall network’s energy efficiency, where multiple femto … DQN algorithm in deep
reinforcement learning to solve it with … can not only achieve better energy efficiency than Q-learning …

… energy efficiency while allocating power. In [25], a multi-agent reinforcement learning solution
using a deep … Therefore, in this paper, we propose an energy-efficient deep reinforcement …

… In this paper, the problem of energy efficiency optimization is studied … energy harvesting
technologies. The goal of this proposed framework is to maximize the average energy efficiency …

… first successful use of a combination of deep neural network and Q-learning algorithm. This
work is responsible for the rapid growth of the field of deep reinforcement learning (DRL). In …