FedGuide is a federated reinforcement learning framework that tackles distributional mismatch among heterogeneous clients by using diffusion priors as personalized behavior models. It aggregates these priors via an Optimal-Transport Mixture-of-Experts, preserving diverse behavior modes, and introduces a Distribution Correction Estimation (DICE) value baseline to give low‑variance, return‑aware guidance for local policy improvement. Experiments on heterogeneous environments demonstrate that FedGuide surpasses existing FRL methods in client‑average returns, final‑round performance, and worst‑round robustness while maintaining stable learning under stronger heterogeneity.
arXiv:2608. 14430v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training provides a direct way to align diffusion models with human preferences and task-specific rewards.
arXiv:2602. 18291v2 Announce Type: replace Abstract: Online Multi-Agent Reinforcement Learning (MARL) is a prominent framework for efficient agent coordination.
arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.
arXiv:2606. 30376v1 Announce Type: new Abstract: Aligning generative flow models on continuous spaces via online reinforcement learning is constrained by intractable trajectory likelihoods.
arXiv:2504. 12742v2 Announce Type: replace Abstract: Decentralized Federated Learning (DFL) enables collaborative model training without relying on a central server.
Diffusion and flow policies can model complex behaviors in offline reinforcement learning (RL). However, penalizing their KL divergence from the behavior policy can discourage actions having high crit...
The paper introduces SCOUT, an offline multi-agent reinforcement learning framework that combines a generative behavioral prior with a decomposed value function for test-time action refinement. SCOUT uses optimal unified transport and Stein variational gradient descent to steer behavioral samples toward high-value regions, with the number of transport steps providing adaptive scaling. The authors prove a single-term KL bound on the joint soft-value gap under the individual-global-max principle and demonstrate that SCOUT outperforms existing methods on both discrete and continuous offline MARL benchmarks, including offline-to-online settings.
arXiv:2610.09091v1 Announce Type: new Abstract: Decision-focused learning (DFL) trains predictive models for downstream optimization, but existing methods largely assume centralized data. In cross-si...
arXiv:2606. 15048v1 Announce Type: new Abstract: Diffusion models are typically trained with objectives that focus on local denoising targets at individual time steps (or adjacent pairs), which do not enforce consistency between predictions along the denoising trajectory.
arXiv:2609.31882v2 Announce Type: replace-cross Abstract: Reward-based diffusion fine-tuning faces practical challenges when desirable outcomes are rare or conditioning corrections are costly to esti...
arXiv:2610.16275v1 Announce Type: cross Abstract: Flow Matching (FM) has recently emerged as a principled and efficient generative modeling framework for reinforcement learning (RL), enabling express...
arXiv:2607. 16090v1 Announce Type: cross Abstract: Transferring policies across domains poses a vital challenge in reinforcement learning, due to the dynamics mismatch between the source and target domains.