[Paper] Forecasting in Offline Reinforcement Learning for Non-stationary Environments

Source: arXiv - 2512.01987v1

Overview

Offline Reinforcement Learning (RL) promises to turn static datasets into high‑performing policies without costly on‑line interaction. The new FORL framework tackles a glaring blind spot: most offline RL methods assume the world stays the same, yet real‑world systems (robots, finance, IoT) often experience abrupt, time‑varying shifts that make the environment partially observable. FORL blends diffusion‑based state generation with zero‑shot time‑series forecasting to give agents a “look‑ahead” on possible future dynamics, enabling robust decision‑making from the very first step of an episode.

Key Contributions

• Unified forecasting pipeline that couples a conditional diffusion model (for generating plausible future states) with off‑the‑shelf zero‑shot time‑series foundation models.
• Pattern‑agnostic training: the diffusion model learns to predict candidate states without any prior assumption about the shape or frequency of non‑stationary offsets.
• Zero‑shot adaptation: no extra fine‑tuning is required on the target non‑stationary data; the forecasting component works out‑of‑the‑box.
• Benchmark augmentation: standard offline RL suites are enriched with real‑world time‑series perturbations (e.g., sensor drift, market shocks) to evaluate non‑stationary robustness.
• Consistent performance gains over strong baselines (CQL, IQL, BCQ) across multiple domains, demonstrating the practical value of forecasting‑augmented policies.

Methodology

1. Data Preparation – The offline dataset is split into (state, action, reward, next‑state) tuples as usual. Additionally, a parallel time‑series stream (e.g., sensor readings, market indices) that captures the hidden non‑stationary factor is collected.
2. Conditional Diffusion Model – A diffusion network is trained to generate candidate future states conditioned on the current state and the observed time‑series context. Because diffusion models iteratively denoise random noise, they can model complex, multimodal future distributions without committing to a single deterministic prediction.
3. Zero‑Shot Forecasting – A pretrained time‑series foundation model (e.g., a large transformer trained on millions of sensor/financial series) receives the recent context and produces a short‑term forecast of the hidden offset. This forecast is fed as an additional conditioning variable to the diffusion model.
4. Policy Integration – The offline RL algorithm (e.g., CQL) receives the diffusion‑generated candidate states as augmented inputs during policy evaluation. The agent selects actions that maximize expected return under the distribution of plausible future states, effectively “planning” for the unknown shift.
5. Inference (Zero‑Shot) – At test time, the pipeline runs end‑to‑end: the foundation model forecasts the offset, the diffusion model samples candidate states, and the policy picks an action—all without any extra training on the new environment.

Results & Findings

• Robustness from episode start: Unlike methods that adapt only after a few steps, FORL already anticipates the shift, reducing the “cold‑start” performance dip.
• Generalization: The same diffusion + forecasting pipeline works across domains with wildly different dynamics (robotics vs. finance) without domain‑specific tuning.
• Ablation: Removing the diffusion component (using only the raw forecast) drops performance by ~5%, confirming that modeling uncertainty in future states is crucial.

Practical Implications

• Deployable offline RL: Companies can train policies on historic logs and safely roll them out in environments that are known to drift (e.g., predictive maintenance, algorithmic trading).
• Zero‑shot adaptability: No need to collect new interaction data or re‑train the RL model when a new sensor is added or a market regime changes—just plug in the latest time‑series forecast.
• Safety‑critical systems: Robots operating in factories with wear‑and‑tear or autonomous vehicles facing weather‑induced sensor bias can benefit from the early‑warning capability, reducing catastrophic failures.
• Toolchain integration: The diffusion model can be implemented with popular libraries (PyTorch, Diffusers) and the forecasting backbone can be any large pretrained transformer (e.g., TimeSeries‑GPT), making the approach compatible with existing ML pipelines.

Limitations & Future Work

• Forecast horizon: The current setup assumes short‑term forecasts (a few seconds or steps). Extending to longer horizons may require hierarchical diffusion or recurrent conditioning.
• Computational overhead: Sampling from diffusion models adds latency; lightweight alternatives (e.g., flow‑based generators) could be explored for real‑time constraints.
• Partial observability: While FORL mitigates hidden offsets, it still relies on the availability of a correlated time‑series signal. Environments lacking such auxiliary data remain challenging.
• Theoretical guarantees: Formal analysis of how forecasting error propagates through the RL objective is an open question the authors plan to address.

Bottom line: FORL shows that marrying modern generative forecasting with offline RL can bridge the gap between static training data and the messy, shifting realities of production systems—opening the door for more reliable, zero‑shot deployable agents.

Authors

• Suzan Ece Ada
• Georg Martius
• Emre Ugur
• Erhan Oztop

Paper Information

• arXiv ID: 2512.01987v1
• Categories: cs.LG, cs.AI, cs.RO
• Published: December 1, 2025
• PDF: Download PDF