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Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation.

, , , , , и . AAAI, стр. 10452-10459. AAAI Press, (2021)

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Jinwei Xu

Yunfei Xing

Georg Xingas

Qiwu Xing

Ye Xing

 

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Adapting to Environment Changes Through Neuromodulation of Reinforcement Learning., , , , и . SAB, том 13499 из Lecture Notes in Computer Science, стр. 115-126. Springer, (2022)Neuromodulated Patience for Robot and Self-Driving Vehicle Navigation., , и . IJCNN, стр. 1-8. IEEE, (2020)Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation., , , , , и . AAAI, стр. 10452-10459. AAAI Press, (2021)Spike trains encoding and threshold rescaling method for deep spiking neural networks., , , и . SSCI, стр. 1-6. IEEE, (2017)Achieving efficient interpretability of reinforcement learning via policy distillation and selective input gradient regularization., , , , и . Neural Networks, (апреля 2023)Uncertainty Aware Model Integration on Reinforcement Learning., , , и . IJCNN, стр. 1-7. IEEE, (2022)CARLsim 6: An Open Source Library for Large-Scale, Biologically Detailed Spiking Neural Network Simulation., , , , , , , , , и . IJCNN, стр. 1-10. IEEE, (2022)CARLsim 4: An Open Source Library for Large Scale, Biologically Detailed Spiking Neural Network Simulation using Heterogeneous Clusters., , , , , , , и . IJCNN, стр. 1-8. IEEE, (2018)