* indicates equal contribution, † indicates correspondence.
Published in IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), March 1, 2025
Authors: Zhile Yang, Shangqi Guo†, Ying Fang, Zhaofei Yu and Jian K. Liu
Summary: We design a recurrent winner-take-all network and propose the spiking variational policy gradient (SVPG), a new R-STDP learning method derived theoretically from the global policy gradient.
Published in IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), April 01, 2023
Authors: Tianren Zhang*, Shangqi Guo*†, Tian Tan, Xiaolin Hu and Feng Chen†
Summary: We proposes an adjacency constraint for goal-conditioned hierarchical reinforcement learning that restricts high-level actions to a k-step adjacent region, theoretically preserving optimality in deterministic MDPs and improving training efficiency in stochastic MDPs, leading to significant performance gains in simulated robotics tasks.
Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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