arXiv Machine Learning By Hany Hamed, Abhishek Naik, Colin Bellinger, A. Rupam Mahmood

Rethinking the Suitability of Reinforcement Learning Algorithms Under Practical Transfer Constraints

Read the original on arXiv Machine Learning →

arXiv:2607. 17326v1 Announce Type: new Abstract: Transfer-oriented reinforcement learning requires evaluating algorithms along dimensions that go beyond standard sample efficiency.

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arXiv Machine Learning
1d ago

Reusing Past Samples in Proximal Policy Optimization: When and How Does It Help?

The paper investigates how reusing past samples can improve the sample efficiency of Proximal Policy Optimization (PPO). Two variants, wPPO-U and wPPO-BH, are introduced within a multiple importance weighting framework, each reusing data from recent iterations while preserving core PPO mechanics. The authors derive theoretical policy improvement bounds for both variants and empirically evaluate their impact on continuous control tasks.

By Alessandro Montenegro, Riccardo Venturelli, Marco Mussi, Matteo Papini, Alberto Maria Metelli
arXiv Machine Learning
Aug 26

WarpSAC: Towards the Pinnacle of Scalable Off-policy RL by Rethinking Exploration and Exploitation

arXiv:2608.24479v1 Announce Type: new Abstract: Massively parallel simulation changes the data regime in which off-policy reinforcement learning (RL) is trained, challenging stabilizers designed for...

By Zihao Wu, Hongyao Tang, Yi Ma, Huizhong Song, Pengyi Li, Yifu Yuan, Fei Ni, Jinyi Liu, Wei Wei, Jianrong Wang, Yan Zheng, Jianye Hao