skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees
Researchers have introduced a new algorithm to estimate structural models of dynamic decisions by human agents, addressing the challenge of high computational complexity. Traditionally, this task involves a nested structure: an inner problem identifying an optimal policy and an outer problem maximizing a measure of fit. Previous methods have struggled with large discrete state spaces or high-dimensional continuous state spaces, often sacrificing reward estimation accuracy. The new approach combines policy improvement with a stochastic gradient step for likelihood maximization, ensuring accurate reward estimation without compromising computational efficiency. This single-loop algorithm, designed to handle high-dimensional state spaces, converges to a stationary solution with finite-time guarantees. When the reward is linearly parameterized, it approximates the maximum likelihood estimator sublinearly, offering a robust solution for complex decision modeling tasks.  more » « less
Award ID(s):
1910385 2414372 2426064
PAR ID:
10546631
Author(s) / Creator(s):
; ;
Publisher / Repository:
INFORMS
Date Published:
Journal Name:
Operations Research
ISSN:
0030-364X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; (, International Joint Conference on Neural Networks)
    Social networks are frequently polluted by rumors, which can be detected by advanced models such as graph neural networks. However, the models are vulnerable to attacks, and discovering and understanding the vulnerabilities is critical to robust rumor detection. To discover subtle vulnerabilities, we design a attacking algorithm based on reinforcement learning to camouflage rumors against black-box detectors. We address exponentially large state spaces, high-order graph dependencies, and ranking dependencies, which are unique to the problem setting but fundamentally challenging for the state-of-the-art end-to-end approaches. We design domain-specific features that have causal effect on the reward, so that even a linear policy can arrive at powerful attacks with additional interpretability. To speed up policy optimization, we devise: (i) a credit assignment method that proportionally decomposes delayed and aggregated rewards to atomic attacking actions for enhance feature-reward associations; (ii) a time-dependent control variate to reduce prediction variance due to large state-action spaces and long attack horizon, based on reward variance analysis and a Bayesian analysis of the prediction distribution. On two real world datasets of rumor detection tasks, we demonstrate: (i) the effectiveness of the learned attacking policy on a wide spectrum of target models compared to both rule-based and end-to-end attacking approaches; (ii) the usefulness of the proposed credit assignment strategy and variance reduction components; (iii) the interpretability of the attacking policy. 
    more » « less
  2. ; ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    In practice, it is essential to compare and rank candidate policies offline before real-world deployment for safety and reliability. Prior work seeks to solve this offline policy ranking (OPR) problem through value-based methods, such as Off-policy evaluation (OPE). However, they fail to analyze special case performance (e.g., worst or best cases), due to the lack of holistic characterization of policies’ performance. It is even more difficult to estimate precise policy values when the reward is not fully accessible under sparse settings. In this paper, we present Probabilistic Offline Policy Ranking (POPR), a framework to address OPR problems by leveraging expert data to characterize the probability of a candidate policy behaving like experts, and approximating its entire performance posterior distribution to help with ranking. POPR does not rely on value estimation, and the derived performance posterior can be used to distinguish candidates in worst-, best-, and average-cases. To estimate the posterior, we propose POPR-EABC, an Energy-based Approximate Bayesian Computation (ABC) method conducting likelihood-free inference. POPR-EABC reduces the heuristic nature of ABC by a smooth energy function, and improves the sampling efficiency by a pseudo-likelihood. We empirically demonstrate that POPR-EABC is adequate for evaluating policies in both discrete and continuous action spaces across various experiment environments, and facilitates probabilistic comparisons of candidate policies before deployment. 
    more » « less
  3. ; ; (, Advances in neural information processing systems)
    Suppose an online platform wants to compare a treatment and control policy (e.g., two different matching algorithms in a ridesharing system, or two different inventory management algorithms in an online retail site). Standard experimental approaches to this problem are biased (due to temporal interference between the policies), and not sample efficient. We study optimal experimental design for this setting. We view testing the two policies as the problem of estimating the steady state difference in reward between two unknown Markov chains (i.e., policies). We assume estimation of the steady state reward for each chain proceeds via nonparametric maximum likelihood, and search for consistent (i.e., asymptotically unbiased) experimental designs that are efficient (i.e., asymptotically minimum variance). Characterizing such designs is equivalent to a Markov decision problem with a minimum variance objective; such problems generally do not admit tractable solutions. Remarkably, in our setting, using a novel application of classical martingale analysis of Markov chains via Poisson's equation, we characterize efficient designs via a succinct convex optimization problem. We use this characterization to propose a consistent, efficient online experimental design that adaptively samples the two Markov chains. 
    more » « less
  4. ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    We study offline reinforcement learning (RL) with heavy-tailed reward distribution and data corruption: (i) Moving beyond subGaussian reward distribution, we allow the rewards to have infinite variances; (ii) We allow corruptions where an attacker can arbitrarily modify a small fraction of the rewards and transitions in the dataset. We first derive a sufficient optimality condition for generalized Pessimistic Value Iteration (PEVI), which allows various estimators with proper confidence bounds and can be applied to multiple learning settings. In order to handle the data corruption and heavy-tailed reward setting, we prove that the trimmed-mean estimation achieves the minimax optimal error rate for robust mean estimation under heavy-tailed distributions. In the PEVI algorithm, we plug in the trimmed mean estimation and the confidence bound to solve the robust offline RL problem. Standard analysis reveals that data corruption induces a bias term in the suboptimality gap, which gives the false impression that any data corruption prevents optimal policy learning. By using the optimality condition for the generalized PEVI, we show that as long as the bias term is less than the ``action gap'', the policy returned by PEVI achieves the optimal value given sufficient data. 
    more » « less
  5. ; ; ; (, Proceedings of the International Conference on Automated Planning and Scheduling)
    We study the problem of inverse reinforcement learning (IRL), where the learning agent recovers a reward function using expert demonstrations. Most of the existing IRL techniques make the often unrealistic assumption that the agent has access to full information about the environment. We remove this assumption by developing an algorithm for IRL in partially observable Markov decision processes (POMDPs). The algorithm addresses several limitations of existing techniques that do not take the information asymmetry between the expert and the learner into account. First, it adopts causal entropy as the measure of the likelihood of the expert demonstrations as opposed to entropy in most existing IRL techniques, and avoids a common source of algorithmic complexity. Second, it incorporates task specifications expressed in temporal logic into IRL. Such specifications may be interpreted as side information available to the learner a priori in addition to the demonstrations and may reduce the information asymmetry. Nevertheless, the resulting formulation is still nonconvex due to the intrinsic nonconvexity of the so-called forward problem, i.e., computing an optimal policy given a reward function, in POMDPs. We address this nonconvexity through sequential convex programming and introduce several extensions to solve the forward problem in a scalable manner.This scalability allows computing policies that incorporate memory at the expense of added computational cost yet also outperform memoryless policies. We demonstrate that, even with severely limited data, the algorithm learns reward functions and policies that satisfy the task and induce a similar behavior to the expert by leveraging the side information and incorporating memory into the policy. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email