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Improving Offline RL by Blending Heuristics
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Proposes Heuristic Blending (HUBL), which relabels offline datasets by blending Monte-Carlo return heuristics into rewards and shrinking discounts, improving four bootstrapping-based offline RL methods by about 9% on 27 D4RL and Meta-World datasets, with a finite-sample analysis.
Problem
Bootstrapping-based model-free offline RL methods perform inconsistently across datasets, sometimes underperforming behavior cloning, due to errors from bootstrapping and function approximation with limited data.
Approach
Relabel offline data with rewards r + gamma*lambda*h and discount gamma*(1-lambda), where h is the Monte-Carlo return of the behavior policy and lambda is a trajectory-dependent blending factor (large for high-return trajectories), so the base offline RL algorithm relies less on bootstrapping where the behavior policy performs well; theory frames this as a reshaped MDP with a bias-regret trade-off
Key results
- HUBL improves four base offline RL methods (CQL, TD3+BC, IQL, ATAC) by about 9% on average across 27 datasets of D4RL and Meta-World.
- On some datasets where base methods are inconsistent, HUBL yields more than 50% relative improvement.
- Rank blending wins 46 of 108 cases versus 30 (sigmoid) and 32 (constant); discount-only ablations without reward relabeling give negative or small gains for ATAC, IQL and TD3+BC.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add reward relabeling to discount-only rank blending (ablation) | ATAC · 27 datasets, average | relative improvement: -0.02 -> 0.02 | Helped |
| Add reward relabeling to discount-only rank blending (ablation) | CQL · 27 datasets, average | relative improvement: 0.04 -> 0.16 | Helped |
| Add reward relabeling to discount-only rank blending (ablation) | IQL · 27 datasets, average | relative improvement: -0.04 -> 0.09 | Helped |
| Add reward relabeling to discount-only rank blending (ablation) | TD3+BC · 27 datasets, average | relative improvement: -0.03 -> 0.09 | Helped |
| Use rank blending instead of sigmoid or constant blending | HUBL · 108 cases across 27 datasets and 4 base methods | wins: 30 vs 46 vs 32 | Helped |
Surprising findings
- Shrinking the discount alone (without reward blending) can hurt performance for IQL and TD3+BC, showing the reward modification is essential.
- Base methods can underperform behavior cloning on some datasets, which HUBL helps mitigate.
Limitations
- Theory is for tabular VI-LCB with simplifying assumptions (constant lambda on support), not the continuous-state deep RL setting.
- Only one hyperparameter alpha is tuned per base method, selected from {0.001, 0.01, 0.1}; some datasets show slight degradation on near-optimal expert data.
- Extension to model-based offline RL is not evaluated.
Experimental setup
Datasets: D4RL Meta-World
Benchmarks: D4RL Meta-World
Metrics: relative normalized score improvement normalized score
Baselines: CQL TD3+BC IQL ATAC discount regularization behavior cloning
Builds on: CQL TD3+BC IQL ATAC VI-LCB
Closest papers by meaning
- Offline RL Policies Should Be Trained to be Adaptive (ICML 2022 · similarity 0.90)
- Is Value Learning Really the Main Bottleneck in Offline RL? (NeurIPS 2024 · similarity 0.88)
- Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees (ICLR 2024 · similarity 0.88)
- Horizon Reduction Makes RL Scalable (NeurIPS 2025 · similarity 0.88)
- Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian (ICLR 2023 · similarity 0.87)
- A Tractable Inference Perspective of Offline RL (NeurIPS 2024 · similarity 0.86)
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