The paper introduces Bidirectional Behavior Prior Distillation (B2PD), a method that uses action‑value priors to train a conditional variational autoencoder for generating high‑value behavior support. These expert behavior priors are then distilled into the online reinforcement learning agent, reducing inefficient exploration and stabilizing policy updates. Experiments on state‑ and pixel‑based tasks show that B2PD improves sample efficiency while maintaining stable learning dynamics.
By Gong Gao, Xiao Lai, Jiaji Shen, Ning Jia, Xianhui Liu, Weidong Zhao
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
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
arXiv:2606. 06967v1 Announce Type: new Abstract: Generative policies provide expressive and multimodal action distributions, making them attractive for reinforcement learning (RL) in complex continuous-control tasks.
By Ke Hu, Shutong Ding, Panxin Tao, Jingya Wang, Ye Shi
The paper introduces Multi-step Proximal Policy Improvement (MPI), a method that refines offline reinforcement learning policies through sequential re-centered proximal steps. By modeling policies as a probability manifold, MPI interprets a wide range of offline actor objectives as a single proximal policy improvement step and extends this to multiple steps for controlled policy improvement beyond the behavior distribution. Experiments on D4RL benchmarks demonstrate that a few MPI refinements enhance strong offline baselines such as TD3+BC, ReBRAC, and IQL across many tasks, while diagnostics clarify the benefits of re-centered refinement over fixed-objective scheduling and highlight critic error limitations.
arXiv:2606. 29526v1 Announce Type: new Abstract: Reinforcement learning (RL) has gained growing attention in large language model (LLM) post-training, yet RL training remains fragile and can suffer from instability or collapse.
By Jing Liang, Hongyao Tang, Yi Ma, Yancheng He, Weixun Wang, Xiaoyang Li, Ju Huang, Wenbo Su, Jinyi Liu, Yan Zheng, Jianye Hao, Bo Zheng
The paper introduces Multi-step Proximal Policy Improvement (MPI), a method that refines offline reinforcement learning policies through sequential re-centered proximal steps. By viewing policies as a probability manifold, MPI interprets a wide range of offline actor objectives as a single proximal policy improvement step and extends this to multiple steps for controlled policy improvement beyond the behavior distribution. Experiments on D4RL benchmarks demonstrate that a few MPI refinements enhance strong offline baselines such as TD3+BC, ReBRAC, and IQL, while diagnostics clarify the benefits of re-centered refinement over fixed-objective scheduling and highlight critic error limitations.
By Soohyun Choi, Seonvin Cho, Songnam Hong
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
arXiv:2606. 02194v1 Announce Type: new Abstract: Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for dexterous manipulation.
By Christian Scherer, Joe Watson, Theo Gruner, Daniel Palenicek, Ingmar Posner, Jan Peters
arXiv:2608.23939v1 Announce Type: new
Abstract: Offline reinforcement learning is intrinsically multi-objective: a policy must remain compatible with the behavioral support of a fixed dataset while p...
By Xiewei Ni, Ruofeng Mei, Xiangyu Xu
arXiv:2607. 20834v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (RL) holds the promise of learning general-purpose policies from static datasets.
By Ahad Jawaid
arXiv:2608. 20208v1 Announce Type: new Abstract: Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction.
By Shaoxuan Wang, Guangting Zheng, Rui Huang, Zhipeng Tang, Sha Zhang, Jiajun Deng, Yanyong Zhang