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The neurobiology of deep reinforcement learning [PDF]
Current Biology, 2020 In this primer, Ölveczky and Gershman review concepts and advances in deep reinforcement learning and discuss how these can inform the implementation of learning processes in biological neural networks.
Samuel J, Gershman, Bence P, Ölveczky
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Tongxin xuebao, 2023 To solve the problem that intelligent devices equipped with deep reinforcement learning agents lack effective security data sharing mechanisms in the intelligent Internet of things, a general federated reinforcement learning (GenFedRL) framework was ...
Biao JIN +4 more
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Steiner tree: a deep reinforcement learning approach
, 2021 Tong, GuangmoThe Steiner tree problem is a classical combinatorial optimization problem that targets interconnecting a set of points by a network whose total length is the shortest, where the network consists of the original points and the newly added ...
Wang, Siqi
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Inductive biases and generalisation for deep reinforcement learning [PDF]
, 2021 In this thesis we aim to improve generalisation in deep reinforcement learning. Generalisation is a fundamental challenge for any type of learning, determining how acquired knowledge can be transferred to new, previously unseen situations.
Igl, Maximilian
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Deep Reinforcement and InfoMax Learning
CoRR, 2020 NeurIPS ...
Bogdan Mazoure +4 more
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Reinforcement learning in populations of spiking neurons [PDF]
, 2008 Population coding is widely regarded as a key mechanism for achieving reliable behavioral responses in the face of neuronal variability. But in standard reinforcement learning a flip-side becomes apparent.
Urbanczik, R +3 more
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Target‐driven visual navigation in indoor scenes using reinforcement learning and imitation learning
CAAI Transactions on Intelligence Technology, 2022 Here, the challenges of sample efficiency and navigation performance in deep reinforcement learning for visual navigation are focused and a deep imitation reinforcement learning approach is proposed.
Qiang Fang +3 more
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Deep Reinforcement Learning methods for StarCraft II Learning Environment [PDF]
, 2022 Reinforcement Learning (RL) is a Machine Learning framework in which an agent learns to solve a task by trial-and-error interaction with the surrounding environment.
Dainese, Nicola
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Deep Reinforcement Learning That Matters
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress.
Peter Henderson 0002 +5 more
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Off-policy Maximum Entropy Deep Reinforcement Learning Algorithm Based on RandomlyWeighted Triple Q -Learning [PDF]
Jisuanji kexue, 2022 Reinforcement learning is an important branch of machine learning.With the development of deep learning,deep reinforcement learning research has gradually developed into the focus of reinforcement learning research.Model-free off-policy deep ...
FAN Jing-yu, LIU Quan
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