Title: ReFORM: Reflected Flows for On-support Offline RL via Noise Manipulation
Primary area: reinforcement learning
Keywords: Offline reinforcement learning, support constraint, flow model

Abstract: Offline reinforcement learning (RL) aims to learn the optimal policy from a fixed
behavior policy dataset without additional environment interaction. One common challenge that arises in this setting is the out-of-distribution (OOD) error,
which occurs when the policy leaves the training distribution. Prior methods penalize a statistical distance term to keep the policy close to the behavior policy, but
this constrains policy improvement and may not completely prevent OOD actions.
Another challenge is that the optimal policy distribution can be multimodal and
difficult to represent. Recent works apply diffusion or flow policies to address this
problem, but it is unclear how to avoid OOD errors while retaining policy expressiveness. We propose ReFORM, an offline RL method based on flow policies that
enforces the less restrictive support constraint by construction. ReFORM learns a
BC flow policy with a bounded source distribution to capture the support of the
action distribution, then optimizes a reflected flow that generates bounded noise
for the BC flow while keeping the support, to maximize the performance. Across
40 challenging tasks from the OGBench benchmark with datasets of varying quality and using a constant set of hyperparameters for all tasks, ReFORM dominates all baselines with hand-tuned hyperparameters on the performance profile curves.

TL;DR: Considering offline RL, we propose ReFORM, a two-stage flow policy that realizes the support constraint by construction and avoids the OOD issue without constraining the policy improvement.