Urban travel carbon emission mitigation approach using deep reinforcement learning. [PDF]
Sci RepShen J, Zheng F, Ma Y, Deng W, Zhang Z.
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Deep Reinforcement Learning-Enabled Computation Offloading: A Novel Framework to Energy Optimization and Security-Aware in Vehicular Edge-Cloud Computing Networks. [PDF]
Sensors (Basel)Almuseelem W.
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RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning. [PDF]
Front NeurorobotJing Y, Weiya L.
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Deep reinforcement learning based low energy consumption scheduling approach design for urban electric logistics vehicle networks. [PDF]
Sci RepSun P +6 more
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Drone-assisted adaptive object detection and privacy-preserving surveillance in smart cities using whale-optimized deep reinforcement learning techniques. [PDF]
Sci RepAbu-Khadrah A +4 more
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A skipping spectrum sensing scheme based on deep reinforcement learning for transform domain communication systems. [PDF]
Sci RepLi C +5 more
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This article is about deep learning (DL) and deep reinforcement learning (DRL) works applied to robotics. Both tools have been shown to be successful in delivering data-driven solutions for robotics tasks, as well as providing a natural way to develop an end-to-end pipeline from the robot’s sensing to its actuation, passing through the generation of a ...
Eduardo F. Morales +5 more
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Learning to Drive with Deep Reinforcement Learning [PDF]
2021 13th International Conference on Knowledge and Smart Technology (KST), 2021 Autonomous driving cars are important due to improved safety and fuel efficiency. Various techniques have been described to consider only a single task, for example, recognition, prediction, and planning with supervised learning techniques. Some limitations of previous studies are: (1) human bias from human demonstration; (2) the need for multiple ...
Nut Chukamphaeng +3 more
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Reinforcement Learning and Deep Reinforcement Learning
2019In order to better understand state-of-the-art reinforcement learning agent, deep Q-network, a brief review of reinforcement learning and Q-learning are first described. Then recent advances of deep Q-network are presented, and double deep Q-network and dueling deep Q-network that go beyond deep Q-network are also given.
F. Richard Yu, Ying He
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This chapter starts by covering the basic concepts involved in reinforcement learning and then describes how to solve reinforcement learning tasks by using basic and deep learning-based solutions. It also provides a brief overview of the typical algorithms central to the deep learning-based solutions, namely DQN, DDPG, and A3C.
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