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Forecasting in Offline Reinforcement Learning for Non-stationary Environments
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Proposes FORL, which combines a conditional diffusion model trained on stationary offline RL data with a zero-shot time-series forecaster (Lag-Llama) and a Dimension-wise Closest Match fusion to estimate hidden per-episode observation offsets at test time.
Problem
Offline RL policies fail when test-time observations are shifted by unknown, episodic, time-varying additive offsets that make the agent partially observable and uncertain of its state, with no ground-truth offsets available during evaluation.
Approach
Fuses multimodal candidate states from a diffusion model trained only on stationary offline data (conditioned on recent action and delta-observation history) with unimodal probabilistic offset forecasts from a zero-shot time-series foundation model, using a per-dimension nearest-sample rule (DCM) to estimate the true state.
Key results
- On maze2d-medium, FORL averages 74.1 normalized score versus 10.0 for DQL and 37.2 for DQL with Lag-Llama mean forecasts.
- With RORL policy on maze2d-large, FORL reaches 71.9 average versus 21.6 for RORL with Lag-Llama mean forecasts.
- The DM-only variant gives a 151.4% relative improvement over DQL without any past offsets, and the full FORL gives a further 97.8% over DM alone.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use FORL instead of DQL + mean Lag-Llama forecast | DQL policy on maze2d-medium · maze2d-medium (five-domain average) | normalized score: 37.2 -> 74.1 | Helped |
| Use FORL instead of Dmbp + Lag-Llama | DQL policy on maze2d-large · maze2d-large (five-domain average) | normalized score: 12.1 -> 38.8 | Helped |
| Use FORL instead of Dmbp + Lag-Llama | DQL policy on antmaze-umaze-diverse · antmaze-umaze-diverse (five-domain average) | normalized score: 51.4 -> 75.1 | Helped |
| Use FORL instead of DQL + mean Lag-Llama forecast | DQL policy on antmaze-medium-diverse · antmaze-medium-diverse (five-domain average) | normalized score: 42.1 -> 48.7 | Helped |
| Use FORL instead of DQL + mean Lag-Llama forecast | DQL policy on antmaze-large-diverse · antmaze-large-diverse (five-domain average) | normalized score: 12.0 -> 34.6 | Helped |
| Use FORL instead of DQL + mean Lag-Llama forecast | DQL policy on kitchen-complete · kitchen-complete (five-domain average) | normalized score: 18.1 -> 21.3 | Helped |
| Use FORL-F instead of FQL + mean Lag-Llama forecast | FQL policy on cube-single-play · cube-single-play (five-domain average) | normalized score: 5.2 -> 45.7 | Helped |
| Use FORL-F instead of FQL + mean Lag-Llama forecast | FQL policy on antmaze-large-navigate · antmaze-large-navigate (five-domain average) | normalized score: 21.2 -> 42.2 | Helped |
| Use RORL rather than DQL as FORL's underlying policy | FORL on maze2d-large · maze2d-large (five-domain average) | normalized score: 38.8 -> 71.9 | Helped |
| Use the diffusion model with DCM fusion rather than the diffusion model alone (ablation) | FORL state estimation · no access to past offsets | normalized score: +97.8% relative performance improvement | Helped |
| Use the diffusion model alone rather than DQL (ablation) | FORL-Dm · no access to past offsets | normalized score: +151.4% | Helped |
| Add DCM fusion of forecast samples and diffusion candidates (ablation) | FORL state estimation · no access to past offsets | normalized score | Helped |
Surprising findings
- Simply subtracting the mean of zero-shot forecasts from observations often gives little or no gain over the base policy.
- Robust offline RL policies (RORL) alone do not help under evolving observation offsets, but combined with FORL they yield large gains.
Limitations
- Limited to additive observation perturbations.
- Forecasting relies on accuracy of the zero-shot time-series model and on offset history being available at intervals.
Experimental setup
Datasets: D4RL maze2d D4RL antmaze D4RL kitchen OGBench cube-single-play OGBench antmaze-large-navigate real-world time-series datasets (real-data-A to E)
Benchmarks: D4RL maze2d D4RL antmaze D4RL kitchen OGBench cube-single-play OGBench antmaze-large-navigate
Metrics: normalized score state prediction error (l2)
Baselines: DQL FQL DQL+Lag-Llama mean DMBP+Lag-Llama TD3BC RORL IQL
Builds on: Diffusion Q-learning Lag-Llama DMBP RORL TD3+BC Flow Q-learning
Closest papers by meaning
- Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning (AAAI 2024 · similarity 0.91)
- From Static Policies to Adaptive Priors in Offline Reinforcement Learning (NeurIPS 2026 · similarity 0.91)
- A Clean Slate for Offline Reinforcement Learning (NeurIPS 2025 · similarity 0.91)
- Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees (ICLR 2024 · similarity 0.90)
- Offline RL with Observation Histories: Analyzing and Improving Sample Complexity (ICLR 2024 · similarity 0.90)
- Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and Evaluation (NeurIPS 2024 · similarity 0.90)
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