5 papers
A Survey of Flow Matching in Reinforcement Learning
Nabuat Zaman Nahim, Fairoz Nower Khan, Peizhong Ju
Flow Matching (FM) has recently emerged as a principled and efficient generative modeling framework for reinforcement learning (RL), enabling expressive, multimodal policy paramete…
Flow Matching with Missing Data
Fairoz Nower Khan, Nabuat Zaman Nahim, Peizhong Ju
Flow matching assumes fully observed training data, which many real-world applications rarely provide. We study Missing-Data Flow Matching, which treats the missing coordinates of…
Discrete MeanFlow: One-Step Generation via Conditional Transition Kernels
Fairoz Nower Khan, Nabuat Zaman Nahim, Md Sajid Ahmed +2
MeanFlow enables one-step generation in continuous spaces by learning an average velocity over a time interval rather than the instantaneous velocity field of flow matching. Howeve…
Discrete Flow Matching for Offline-to-Online Reinforcement Learning
Fairoz Nower Khan, Nabuat Zaman Nahim, Peizhong Ju
Many reinforcement learning (RL) tasks have discrete action spaces, but most generative policy methods based on diffusion and flow matching are designed for continuous control. Mea…
Flow Matching for Offline Reinforcement Learning with Discrete Actions
Fairoz Nower Khan, Nabuat Zaman Nahim, Ruiquan Huang +2
Generative policies based on diffusion models and flow matching have shown strong promise for offline reinforcement learning (RL), but their applicability remains largely confined…