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Adjacency Constraint for Efficient Hierarchical Reinforcement Learning

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.

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Paper Title Number 4

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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Spiking Variational Policy Gradient for Brain Inspired Reinforcement Learning

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.

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Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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