Short-Long Policy Evaluation with Novel Actions (2407.03674v2)

Abstract: From incorporating LLMs in education, to identifying new drugs and improving ways to charge batteries, innovators constantly try new strategies in search of better long-term outcomes for students, patients and consumers. One major bottleneck in this innovation cycle is the amount of time it takes to observe the downstream effects of a decision policy that incorporates new interventions. The key question is whether we can quickly evaluate long-term outcomes of a new decision policy without making long-term observations. Organizations often have access to prior data about past decision policies and their outcomes, evaluated over the full horizon of interest. Motivated by this, we introduce a new setting for short-long policy evaluation for sequential decision making tasks. Our proposed methods significantly outperform prior results on simulators of HIV treatment, kidney dialysis and battery charging. We also demonstrate that our methods can be useful for applications in AI safety by quickly identifying when a new decision policy is likely to have substantially lower performance than past policies.

• The paper presents a novel framework that predicts long-term outcomes from short-term observations, addressing temporal evaluation challenges.
• It proposes SLEV and SLED algorithms that leverage supervised learning and Markov dynamics to efficiently evaluate policies with novel actions.
• Empirical results across healthcare, education, and energy simulations show improved prediction accuracy and early risk detection compared to baselines.

An Overview of "Short-Long Policy Evaluation with Novel Actions"

The paper "Short-Long Policy Evaluation with Novel Actions" proposes a new framework within the domain of sequential decision-making tasks, targeted explicitly at evaluating the long-term outcome performance of decision policies incorporating new actions using their short-term observations. This novel evaluation setting directly addresses the challenge posed by the time lag between policy implementation and observable outcomes, making it a valuable contribution to various real-world applications such as healthcare, education, and energy management.

Problem Setting and Motivations

The authors frame the problem of policy evaluation in scenarios where human designers frequently introduce novel interventions, which can only be evaluated over extended horizons. For example, evaluating the impact of a new teaching method would ideally require an entire academic year, mirroring similar temporal constraints observed in healthcare and other fields. However, conducting such long-term evaluations is often impractical, expensive, or infeasible in terms of time. Thus, the primary research question is whether it is feasible to estimate the long-term outcomes of new decision policies from short-term data and using historical data from prior policies.

Proposed Methods

The paper introduces two key algorithms, SLEV (Short-Long Estimation of Value) and SLED (Short-Long Estimation of Dynamics), aimed at addressing this evaluation challenge.

1. SLEV (Short-Long Estimation of Value)

The SLEV algorithm casts the evaluation problem as a supervised learning task under a known distribution shift. This method relies on historical data of past policies and their long-term performances to train a predictive model. Specifically:

• Supervised Learning with Distribution Shift: The approach involves learning a function that predicts the long-term return of a policy based on short-term trajectories. SLEV incorporates density ratio weighting to address distribution mismatches between the historical and target policy data.
• General Applicability: SLEV makes no strict assumptions about underlying Markov properties, making it broadly applicable across different domains.

2. SLED (Short-Long Estimation of Dynamics)

The SLED algorithm leverages the Markov structure inherent to many decision-making tasks to improve policy evaluation accuracy:

• Markov Dynamics Exploitation: This method focuses on learning a low-dimensional parameterization of the dynamics model using historical data and then fine-tuning it with short-term observations from the target policy.
• Parameter Efficient Fine-Tuning: By minimizing the parameter space that needs adjustment given the short-horizon data, SLED achieves sample-efficient fine-tuning, making it practical even with limited new data.

Empirical Evaluation

The proposed methods were evaluated on simulators of HIV treatment, kidney dialysis, and battery charging – three domains where the long-term consequences of decision policies are crucially important. The results show:

1. Prediction Accuracy: SLEV outperforms baseline methods, including Fitted Q-Evaluation (FQE), online dynamics modeling, and linear extrapolation, particularly for short horizon lengths (up to 25% of the full horizon).
2. Early Risk Detection: The models also demonstrate superior capability in early detection of suboptimal policies, which is critical for preventing potential adverse consequences in real-world deployments.
3. Distribution Shift Considerations: Comparisons under varying distribution shift scenarios reveal SLED's robustness in leveraging Markov assumptions to maintain high prediction accuracy despite significant train-test distribution mismatches.

Theoretical Contributions

The paper provides a theoretical analysis underpinning the SLEV algorithm, offering guarantees on generalization performance under distribution shifts. The results present bounds on the expected performance of the prediction model applied to unseen policies using density-ratio-weighted regression, making robust, theoretically sound claims about its applicability.

Practical and Theoretical Implications

Practically, the proposed framework has the potential to significantly expedite the development and evaluation of decision-making policies in various fields by allowing practitioners to gain early insights into the long-term performance implications of new interventions. Theoretically, the introduction of the short-long evaluation framework broadens the scope of policy evaluation methodologies by introducing novel algorithmic considerations tailored to real-world constraints.

Future Directions

Anticipating future developments, the following areas stand out:

1. Policy Optimization under Short-Long Evaluation: Extending this framework to policy optimization poses an intriguing challenge and potential for impactful applications.
2. Action Embeddings Incorporation: Exploring action embeddings to better handle novel actions and leverage feature overlaps.
3. Infrastructure and Deployment: Translating these algorithms for real-time deployment in operational environments where rapid policy evaluation is crucial.

Conclusion

The paper presents a thoughtful and detailed methodology for addressing the critical issue of long-horizon policy evaluation using short-term data enriched by historical trajectories. By proposing robust algorithms backed by theoretical guarantees and demonstrating their efficacy through rigorous experiments, this work sets the stage for innovative approaches to real-world decision-making under temporal constraints.