ReFORM: Reflected Flows for On-support Offline RL via Noise Manipulation
This addresses offline RL challenges for AI systems by providing a method to avoid OOD errors while maintaining policy expressiveness, though it appears incremental as it builds on flow policies.
The paper tackles the problem of out-of-distribution errors and multimodal policy representation in offline reinforcement learning by proposing ReFORM, a method that uses flow policies with bounded noise to enforce support constraints, resulting in dominance over baselines across 40 tasks in the OGBench benchmark.
Offline reinforcement learning (RL) aims to learn the optimal policy from a fixed dataset generated by behavior policies without additional environment interactions. 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 behavior cloning (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.