arXiv Machine Learning June 19, 2026 By Dillon Sandhu, Ronald Parr

Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL

Read the original on arXiv Machine Learning →

arXiv:2605. 05481v2 Announce Type: replace Abstract: We revisit a classic "chicken-and-egg" problem in reinforcement learning: to safely improve a policy, the value function must be accurate on the state-visitation distribution of the updated policy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 30

Collaborative Weighting with Pessimistic Critic for Mitigating Overestimation in Off-Policy Reinforcement Learning

arXiv:2607. 26509v1 Announce Type: new Abstract: Deep off-policy reinforcement learning algorithms for continuous control typically rely on neural value function approximation to guide policy improvement.

By Gong Gao, Xiao Lai, Ziqi Xie, Guojie Chen, Xianhui Liu, Weidong Zhao

reinforcement-learning safety

arXiv Machine Learning

Sep 22

Streaming Deep Reinforcement Learning Finally Works

arXiv:2410.14606v3 Announce Type: replace Abstract: Learning from a stream of experience as it arrives, also known as streaming learning, is a core part of natural learning. However, reliable streami...

By Mohamed Elsayed, Elena Sorina Lupu, Gautham Vasan, A. Rupam Mahmood

reinforcement-learning robotics benchmarks

arXiv Machine Learning

Sep 21

REFINEPPO: Learning Continuous Control Policies by Iterative Action Refinement

arXiv:2609.21108v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) has achieved strong performance across a wide range of continuous-control problems. These continuous-control policies...

By Sachini Weerasekara, Sagar Kamarthi, Jacqueline Isaacs

agents reinforcement-learning benchmarks

arXiv AI

Jun 10

Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning

arXiv:2606. 11087v1 Announce Type: cross Abstract: Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simulated and real robot control.

By Zhiyuan Zhou, Andy Peng, Charles Xu, Qiyang Li, Tobias Springenberg, Kevin Frans, Sergey Levine

diffusion reinforcement-learning robotics benchmarks

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

reinforcement-learning

arXiv Machine Learning

Aug 24

Decoupling Policy Extraction for Offline Reinforcement Learning

arXiv:2608.20909v1 Announce Type: new Abstract: Offline RL methods commonly jointly train the actor and critic, where the critic is used to guide the actor toward higher-value actions. This coupled l...

By Xuyao Lin, Yixiang Shan, Jinru Duan, Tao Yang, Xinyu Zhao, Runyu Lei, Yiming Zhao, Jiaxin Fan, Zongbao Feng, Peng Jia

rag reinforcement-learning

One email a morning, machine-written

Related stories

Collaborative Weighting with Pessimistic Critic for Mitigating Overestimation in Off-Policy Reinforcement Learning

Streaming Deep Reinforcement Learning Finally Works

REFINEPPO: Learning Continuous Control Policies by Iterative Action Refinement

Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning

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

Decoupling Policy Extraction for Offline Reinforcement Learning