Efficient Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy

Core Concepts

The core message of this paper is to introduce a novel doubly-robust off-policy evaluation (OPE) estimator, called DRUnknown, that can efficiently estimate the value of a target policy when both the logging policy and the value function are unknown.

Abstract

The paper presents a new doubly-robust off-policy evaluation (OPE) estimator called DRUnknown for Markov decision processes. The key highlights are: DRUnknown is designed for situations where both the logging policy and the value function are unknown. It simultaneously estimates the logging policy model and the value function model. When the logging policy model is correctly specified, DRUnknown achieves the smallest asymptotic variance within the class of existing OPE estimators that use an estimated logging policy. When the value function model is also correctly specified, DRUnknown is optimal as its asymptotic variance reaches the semiparametric lower bound. The authors derive the influence function of the proposed estimator and use it to estimate the parameters that minimize the asymptotic variance. Experiments on contextual bandits and reinforcement learning problems show that DRUnknown consistently outperforms existing methods in terms of mean-squared error.

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Key Insights Distilled From

Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy

by Kyungbok Lee... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01830.pdf

Deeper Inquiries

How can the proposed DRUnknown estimator be extended to handle continuous action spaces

To extend the proposed DRUnknown estimator to handle continuous action spaces, we can utilize function approximation techniques such as neural networks or kernel methods. Instead of directly estimating the value function for each discrete action, we can estimate a continuous function that maps states and actions to their corresponding values. This continuous action-value function can then be used in the DRUnknown estimator to evaluate policies in environments with continuous action spaces. By employing function approximation, we can generalize the estimator to handle a wider range of action spaces, including continuous ones.

What are the potential limitations of the DRUnknown estimator, and how can it be further improved

One potential limitation of the DRUnknown estimator is its reliance on the correct specification of the logging policy model. If the estimated logging policy deviates significantly from the true logging policy, it can lead to biased estimates and increased variance in the evaluation. To address this limitation, one possible improvement is to incorporate robust estimation techniques that can handle model misspecification. By introducing robust estimators or incorporating regularization methods, the DRUnknown estimator can become more resilient to errors in the logging policy estimation. Another improvement could involve enhancing the efficiency of the estimator by exploring more sophisticated optimization techniques. By optimizing the estimation process to minimize the asymptotic variance more effectively, the DRUnknown estimator can achieve better performance in off-policy evaluation tasks. Additionally, incorporating adaptive mechanisms that dynamically adjust the estimation process based on the data characteristics can further enhance the robustness and efficiency of the estimator.

How can the insights from this work on off-policy evaluation be applied to other areas of reinforcement learning, such as exploration-exploitation trade-offs or safe exploration

The insights from this work on off-policy evaluation can be applied to other areas of reinforcement learning, such as exploration-exploitation trade-offs and safe exploration. By understanding the importance of accurately estimating the value function and the impact of the logging policy on evaluation, we can develop more effective exploration strategies. In exploration-exploitation trade-offs, the knowledge gained from off-policy evaluation can help in designing exploration policies that balance the exploration of new actions with the exploitation of known high-reward actions. By leveraging off-policy evaluation techniques to estimate the value of different exploration strategies, we can improve the efficiency and effectiveness of exploration in reinforcement learning algorithms. Furthermore, in the context of safe exploration, the insights from off-policy evaluation can be used to assess the safety and performance of exploration policies. By evaluating the impact of different exploration strategies on the overall performance of the agent, we can ensure that the exploration process is not only effective in discovering new states but also safe and reliable in real-world applications. This can lead to the development of reinforcement learning algorithms that prioritize safe exploration while maximizing long-term rewards.