Leveraging Expert Demonstrations for Efficient Sequential Decision-Making under Unobserved Heterogeneity

Core Concepts

The core message of this paper is to leverage offline expert demonstrations to infer an informative prior distribution over unobserved task variables, which can then be used to guide efficient exploration in online sequential decision-making tasks.

Abstract

The paper addresses the problem of online sequential decision-making given auxiliary offline expert demonstration data, where the experts' decisions are based on unobserved contextual information. This setting arises in many application domains, such as self-driving cars, healthcare, and finance, where expert demonstrations are made using contextual information that is not recorded in the data available to the learning agent.
The authors model the problem as a zero-shot meta-reinforcement learning setting with an unknown task distribution and a Bayesian regret minimization objective, where the unobserved tasks are encoded as parameters with an unknown prior. They propose the Experts-as-Priors (ExPerior) algorithm, a non-parametric empirical Bayes approach that utilizes the principle of maximum entropy to establish an informative prior over the learner's decision-making problem. This prior enables the application of any Bayesian approach for online decision-making, such as posterior sampling.
The key highlights and insights from the paper are:

Formulation of the problem as a zero-shot meta-RL setting with unobserved heterogeneity, where the learner aims to minimize Bayesian regret.
Derivation of a maximum entropy expert prior using the offline expert demonstration data, which can be used to guide exploration in online decision-making.
Empirical evaluation of ExPerior in multi-armed bandits and reinforcement learning tasks, showcasing its superiority over existing offline, online, and offline-online baselines.
Empirical regret analysis for multi-armed bandits, demonstrating that the Bayesian regret of ExPerior is proportional to the entropy of the optimal action under the prior distribution, aligning with the entropy of expert policy if the experts are optimal.

Stats

The entropy of the optimal action under the prior distribution is an important factor in determining the Bayesian regret of ExPerior in multi-armed bandits.

Quotes

"The core message of this paper is to leverage offline expert demonstrations to infer an informative prior distribution over unobserved task variables, which can then be used to guide efficient exploration in online sequential decision-making tasks."
"The authors model the problem as a zero-shot meta-reinforcement learning setting with an unknown task distribution and a Bayesian regret minimization objective, where the unobserved tasks are encoded as parameters with an unknown prior."
"The key highlights and insights from the paper are: 1) Formulation of the problem as a zero-shot meta-RL setting with unobserved heterogeneity, where the learner aims to minimize Bayesian regret. 2) Derivation of a maximum entropy expert prior using the offline expert demonstration data, which can be used to guide exploration in online decision-making."

Key Insights Distilled From

Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity

by Vahid Balaza... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07266.pdf

Deeper Inquiries

How can the proposed framework be extended to handle non-stationary task distributions, where the unobserved factors may change over time

To extend the proposed framework to handle non-stationary task distributions, where the unobserved factors may change over time, we can introduce a mechanism for online adaptation of the prior distribution. This adaptation can be based on the incoming data and the observed performance of the learner. One approach could involve updating the prior distribution periodically based on the recent expert demonstrations and the learner's experiences. By incorporating a mechanism for updating the prior distribution dynamically, the framework can adapt to changes in the task distribution and the unobserved factors over time. This adaptive approach would allow the framework to maintain relevance and effectiveness in scenarios where the task distribution is non-stationary.

What are the theoretical guarantees on the Bayesian regret of ExPerior, and how do they compare to the standard regret bounds for Thompson sampling under correct priors

The theoretical guarantees on the Bayesian regret of ExPerior can be analyzed in the context of the maximum entropy expert prior and its impact on the learning process. The standard regret bounds for Thompson sampling under correct priors typically exhibit sublinear growth with respect to the number of episodes and the complexity of the task. In the case of ExPerior, the Bayesian regret is proportional to the entropy of the optimal action under the prior distribution derived from expert demonstrations. This relationship between the regret and the entropy provides insights into how the informativeness of the expert data influences the learning efficiency. By understanding the theoretical guarantees of ExPerior in relation to the entropy of the optimal action, we can assess its performance in different task settings and levels of task heterogeneity.

Can the maximum entropy expert prior be further refined by incorporating additional information about the task structure or the expert's decision-making process

The maximum entropy expert prior can be further refined by incorporating additional information about the task structure or the expert's decision-making process. One way to enhance the prior distribution is to introduce task-specific features or context variables that capture relevant information about the tasks. These features can be used to tailor the prior distribution to the specific characteristics of each task, leading to a more informed and effective exploration strategy. Additionally, incorporating insights from the expert's decision-making process, such as the reasoning behind their actions or the strategies they employ, can help refine the prior distribution to better guide the learner in decision-making. By enriching the maximum entropy expert prior with task-specific information and expert knowledge, the framework can improve its ability to leverage expert demonstrations and adapt to diverse decision-making scenarios.