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Provable Zero-Shot Generalization in Offline Reinforcement Learning
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Proves that offline RL without context labels cannot generalize zero-shot, and proposes PERM and PPPO (pessimistic policy evaluation based model-based and model-free methods) with suboptimality bounds for zero-shot generalization across environments.
Problem
Zero-shot generalization of offline reinforcement learning to unseen test environments, using only offline datasets collected from multiple training environments (contextual MDPs), with no theoretical guarantees previously available.
Approach
Two meta-algorithms (PERM, a model-based pessimistic empirical risk minimization over n environments, and PPPO, a model-free pessimistic proximal policy optimization producing one policy per environment) use a pessimistic policy evaluation oracle so that the learned policy's average value over environments is near-optimal; the authors also show that merging datasets without context labels reduces
Key results
- PERM suboptimality bounded by sqrt(log N / n) supervised learning error plus RL error from uncertainty quantifiers over n environments.
- PPPO suboptimality bounded by 10(sqrt(log|A| H^2 / n) + RL error), with alpha = 1/sqrt(H^2 n), holding with probability at least 2/3.
- For linear MDPs with well-explored behavior policies, both methods achieve O(n^{-1/2} + K^{-1/2} C_n^*) convergence to the optimal policy.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use PERM with pessimistic policy evaluation | Offline RL over contextual MDPs · Contextual MDPs, zero-shot generalization | SubOpt upper bound: 7\sqrt{\frac{2\log(6\mathcal{N}_{(Hn)^{-1}}^\Pi/\delta)}{n}} +\frac{2}{n}\sum_{i=1}^n\sum_{h=1}^H\E{i,\pi^*}{\Gamma_{i,h | Helped |
| Use PPPO with pessimistic policy evaluation | Offline RL over contextual MDPs · Contextual MDPs, zero-shot generalization | SubOpt upper bound: 10\bigg(\sqrt{\frac{\log|\mathcal{A}|H^2}{n}} + \frac{1}{n}\sum_{i=1}^n \sum_{h=1}^H\E{i,\pi^*}{\Gamma_{i,h}(s_h,a_h)|s_ | Helped |
| Remove context indicator information and train on the merged dataset | Markovian offline RL · Contextual MDPs, zero-shot generalization | SubOpt: c'' \cdot d^{3/2} H^2 K^{-1/2} \sqrt{\log(4dHK/\delta)}+ 2\sup_\pi |V_{\bar{\cM}, 1}^{\pi}(x_1)-\EE_{c\sim C} V_{c, 1}^{ | Hurt |
| Use well-exploring behavior policies for each environment | PERM and PPPO · Offline linear MDPs | SubOpt upper bound: O(n^{-1/2} + K^{-1/2}\cdot C_n^*) | Helped |
Surprising findings
- Offline RL without context indicators converges to the optimal policy of an average MDP, which can be far from the zero-shot optimal policy (example gives 0 versus 0.5 value).
Limitations
- Results rely on i.i.d. sampling of environments from the context distribution.
- Model-based PERM requires maintaining n models or critics simultaneously, which is impractical for large n (partially addressed by merging contexts into m groups).
- No empirical experiments are reported; results are purely theoretical.
Experimental setup
Metrics: suboptimality gap
Baselines: CQL behavior cloning PEVI
Builds on: PEVI Optimistic model-based ERM PPO CQL
Closest papers by meaning
- Doubly Mild Generalization for Offline Reinforcement Learning (NeurIPS 2024 · similarity 0.91)
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- Optimistic Model Rollouts for Pessimistic Offline Policy Optimization (AAAI 2024 · similarity 0.90)
- Forecasting in Offline Reinforcement Learning for Non-stationary Environments (NeurIPS 2025 · similarity 0.89)
- A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch (NeurIPS 2024 · similarity 0.89)
- The Generalization Gap in Offline Reinforcement Learning (ICLR 2024 · similarity 0.89)
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