arXiv Machine Learning
CoDrift: Compositional Drifting for Offline Reinforcement Learning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
4d ago
Discrete Flow Matching for Offline-to-Online Reinforcement Learning
arXiv:2605.12379v2 Announce Type: replace-cross Abstract: Many reinforcement learning (RL) tasks have discrete action spaces, but most generative policy methods based on diffusion and flow matching a...
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...
arXiv Machine Learning
Sep 1
PathBridger: Subgoal Bridges for Offline Goal-Conditioned Reinforcement Learning
arXiv:2608.29061v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (GCRL) aims to learn policies for reaching diverse goals entirely from fixed trajectory data. Long-hori...
arXiv AI
Oct 7
Variance-Averse $n$-Step Offline Reinforcement Learning for Sparse Long-Horizon Environments
arXiv:2610.07899v1 Announce Type: cross Abstract: Generative actors are transforming offline reinforcement learning (RL) by enabling expressive policy classes that model complex action distributions....
arXiv Machine Learning
Jul 28
Hierarchical Reinforcement Learning with Optimal Level Synchronization Based on Flow-Based Deep Generative Model
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.
arXiv Machine Learning
Jun 16
Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data
arXiv:2502. 19544v3 Announce Type: replace Abstract: Leveraging offline data is a promising way to improve the sample efficiency of online reinforcement learning (RL).