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When is Offline Policy Selection Sample Efficient for Reinforcement Learning? (2312.02355v1)

Published 4 Dec 2023 in cs.LG and cs.AI

Abstract: Offline reinforcement learning algorithms often require careful hyperparameter tuning. Consequently, before deployment, we need to select amongst a set of candidate policies. As yet, however, there is little understanding about the fundamental limits of this offline policy selection (OPS) problem. In this work we aim to provide clarity on when sample efficient OPS is possible, primarily by connecting OPS to off-policy policy evaluation (OPE) and BeLLMan error (BE) estimation. We first show a hardness result, that in the worst case, OPS is just as hard as OPE, by proving a reduction of OPE to OPS. As a result, no OPS method can be more sample efficient than OPE in the worst case. We then propose a BE method for OPS, called Identifiable BE Selection (IBES), that has a straightforward method for selecting its own hyperparameters. We highlight that using IBES for OPS generally has more requirements than OPE methods, but if satisfied, can be more sample efficient. We conclude with an empirical study comparing OPE and IBES, and by showing the difficulty of OPS on an offline Atari benchmark dataset.

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Citations (1)
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Summary

  • The paper demonstrates that in worst-case scenarios, offline policy selection inherits the sample efficiency limits of off-policy evaluation.
  • It introduces Identifiable BE Selection (IBES), leveraging Bellman error estimation to improve sample efficiency under specific conditions.
  • Empirical benchmarks, including Atari experiments, highlight the inherent challenges of OPS, with advanced methods sometimes failing to beat random selection.

Offline reinforcement learning (RL) enables the training of policies from a dataset without the need for active interaction with the environment. This can be particularly valuable in situations where live interaction is impractical or risky. However, successfully employing offline RL techniques often hinges on selecting the right hyperparameters for learning algorithms. This selection process, known as Offline Policy Selection (OPS), has been an under-explored challenge, and a paper seeks to illuminate when and how OPS can be executed in a sample-efficient manner.

The paper builds a link between OPS and two established concepts: off-policy policy evaluation (OPE) and BeLLMan error (BE) estimation. The researchers provide an important theoretical insight by demonstrating that in the worst-case scenario, the difficulty of OPS is equivalent to that of OPE, meaning no OPS method can surpass OPE in sample efficiency in such cases. This affirmation is supported by showing a reduction from OPE to OPS, suggesting that any limitations in OPE are inherited by OPS.

Moving from theory to practice, the paper proposes a new approach for OPS called Identifiable BE selection (IBES). Unlike many OPE methods, IBES comes with its own set of prerequisites that, if met, can lead to improved sample efficiency. The paper includes empirical comparisons of OPE and IBES, using them in OPS to highlight their differing requirements and efficiencies.

The assessments indicate that while BE-based methods and standard OPE approaches such as Fitted Q Evaluation (FQE) can be effective, they have their own challenges, particularly in cases where OPS is inherently difficult or when candidate policies are not well-represented in the offline data. For instance, in experiments with the Atari benchmark dataset, all OPS methods evaluated, including IBES, failed to outperform even random policy selection, underscoring the inherent challenges of OPS.

Understanding these challenges, the paper makes significant strides in clarifying the conditions under which OPS can be effective. It underscores the limitations of existing methods, introduces its own approach with IBES, and calls for further exploration into OPS methodologies and their application in real-world settings. The findings from this paper highlight the complexities of offline RL policy selection and emphasize the need for more nuanced, context-specific approaches for handling offline data and algorithm parameters.

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