Abstract: Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex ...

@article{kostrikov2021iql, title={Offline Reinforcement Learning with Implicit Q-Learning}, author={Ilya Kostrikov and Ashvin Nair and Sergey Levine}, year={2021 ...

Artificial intelligence enhances the network capability with automatic and adaptive adjustment. Reinforcement learning (RL) and deep reinforcement learning (DRL) are two powerful techniques in ...

Neuromorphic hardware with spiking neural network (SNN) architecture utilizes insights from biological phenomena to offer encouraging solutions. Previous studies have proposed reinforcement learning ...

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Traditional reinforcement learning algorithms lack exploration of the environment, and the strategies learned by the agent may not generalize well to other different environments. To address these ...

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Robotic Table Tennis with Model-Free Reinforcement Learning. [pdf][arXiv] To appear in IROS 2020. Xingyou Song, Yuxiang Yang, Krzysztof Choromanski, Ken Caluwaerts, Wenbo Gao, Chelsea Finn, and Jie ...