arXiv:2603. 05296v2 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) allows robots to learn from offline datasets without risky exploration.
By Hokyun Im, Andrey Kolobov, Jianlong Fu, Youngwoon Lee
arXiv:2107. 08183v2 Announce Type: replace Abstract: High-dimensional state and action spaces combined with sparse reward structures in reinforcement learning (RL) environments typically require advanced control architectures.
By JaeYoon Kim, Junyu Xuan, Christy Liang, Farookh Hussain
The paper introduces fixed‑point flow matching (FFM), a data‑free method for training and fine‑tuning flow policies in reinforcement learning. FFM formulates the actor update as a fixed‑point iteration using a flow‑matching regression objective, where the critic’s action gradients act as targets. Experiments show that FFM can effectively fine‑tune pretrained flow policies in offline‑to‑online scenarios and train flow policies from scratch in online RL, establishing it as a principled approach for learning expressive flow‑based policies without training data.
By Denis Blessing, Onur Celik, Gerhard Neumann
arXiv:2603. 25464v2 Announce Type: replace-cross Abstract: Zero-shot reinforcement learning (RL) algorithms aim to learn a family of policies from a reward-free dataset, and recover optimal policies for any reward function directly at test time.
By Jiajun Hu, Nuria Armengol Urpi, Jin Cheng, Stelian Coros
The paper introduces Flow Matching Policy Optimization with Mirror Descent and Entropy Constraints (FMER), a reinforcement learning framework that uses ODE-based flow matching to overcome limitations of SDE-based diffusion policies. FMER derives an analytic entropy objective to correct density distortion from a tanh transformation, establishes that minimizing an advantage-weighted conditional flow matching loss serves as a simulation-free surrogate for policy mirror descent, and dynamically tunes the mirror descent temperature to enforce a robust trust region. Experiments show FMER outperforms baselines on the sparse-reward FrankaKitchen task while remaining competitive on dense-reward MuJoCo benchmarks.
By Ting Gao, Stavros Orfanoudakis, Nan Lin, Winnie Daamen, Serge Hoogendoorn, Elvin Isufi
The paper introduces AnchorQ, a method that improves sample-efficient reinforcement learning for policy refinement by using flow-reversed action anchors. AnchorQ maps reference behaviors to state-conditioned latent anchors, samples local particles around these anchors, and employs value-guided sampling to explore promising refinements. Experiments on simulated and real-world manipulation tasks show that AnchorQ outperforms state-of-the-art post-training baselines, achieving a 37% higher real-world success rate and 97% greater simulated task throughput within the same interaction budget.
By Carl Qi, Weijun Dong, Xiaojie Wang, Peiyi Chen, Rahul Sajnani, Mingyuan Zhang, Amy Zhang, Jose Barreiros, Manikatan Nambi, Lillian J. Ratliff, Dylan Randle