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Social Attentive Deep Q-network for Recommendation
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019While deep reinforcement learning has been successfully applied to recommender systems, it is challenging and unexplored to improve the performance of deep reinforcement learning recommenders by effectively utilizing the pervasive social networks. In this work, we develop a Social Attentive Deep Q-network (SADQN) agent, which is able to provide high ...
Yu Lei +3 more
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Accelerating Deep Q Network by Weighting Experiences
2018Deep Q Network (DQN) is a reinforcement learning methodlogy that uses deep neural networks to approximate the Q-function. Literature reveals that DQN can select better responses than humans. However, DQN requires a lengthy period of time to learn the appropriate actions by using tuples of state, action, reward and next state, called “experience ...
Kazuhiro Murakami +4 more
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Combine Deep Q-Networks with Actor-Critic
2020The deep Q-network algorithm is one of the most well-known deep reinforcement learning algorithms, which combines reinforcement learning with deep neural networks to approximate the optimal action-value functions. It receives only the pixels as inputs and achieves human-level performance on Atari games.
Hongming Zhang +2 more
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Maze Solving Using Deep Q-Network
Advances In Robotics - 6th International Conference of The Robotics Society, 2023Anushtup Nandy +2 more
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Deep Eutectic Solvents: A Review of Fundamentals and Applications
Chemical Reviews, 2021Brian Chen, Yong Zhang, Jeffrey M Klein
exaly
Intelligent ship route planning via an A∗ search model enhanced double-deep Q-network
Ocean EngineeringXinqiang Chen +6 more
semanticscholar +1 more source
Soft wearable devices for deep-tissue sensing
Nature Reviews Materials, 2022Muyang Lin, Hongjie Hu, Sai Zhou
exaly