Learning Optimal and Sample-Efficient Decision Policies with Guarantees

• URL: http://arxiv.org/abs/2602.17978v1
• Date: Fri, 20 Feb 2026 04:24:49 GMT
• Title: Learning Optimal and Sample-Efficient Decision Policies with Guarantees
• Authors: Daqian Shao,
• Abstract summary: This thesis addresses the problem of learning from offline datasets in the presence of hidden confounders.<n>We derive a sample-efficient algorithm for solving conditional moment restrictions problems with convergence and optimality guarantees.<n>We also develop an algorithm that can learn effective imitator policies with convergence rate guarantees.
• Score: 3.096615629099617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The paradigm of decision-making has been revolutionised by reinforcement learning and deep learning. Although this has led to significant progress in domains such as robotics, healthcare, and finance, the use of RL in practice is challenging, particularly when learning decision policies in high-stakes applications that may require guarantees. Traditional RL algorithms rely on a large number of online interactions with the environment, which is problematic in scenarios where online interactions are costly, dangerous, or infeasible. However, learning from offline datasets is hindered by the presence of hidden confounders. Such confounders can cause spurious correlations in the dataset and can mislead the agent into taking suboptimal or adversarial actions. Firstly, we address the problem of learning from offline datasets in the presence of hidden confounders. We work with instrumental variables (IVs) to identify the causal effect, which is an instance of a conditional moment restrictions (CMR) problem. Inspired by double/debiased machine learning, we derive a sample-efficient algorithm for solving CMR problems with convergence and optimality guarantees, which outperforms state-of-the-art algorithms. Secondly, we relax the conditions on the hidden confounders in the setting of (offline) imitation learning, and adapt our CMR estimator to derive an algorithm that can learn effective imitator policies with convergence rate guarantees. Finally, we consider the problem of learning high-level objectives expressed in linear temporal logic (LTL) and develop a provably optimal learning algorithm that improves sample efficiency over existing methods. Through evaluation on reinforcement learning benchmarks and synthetic and semi-synthetic datasets, we demonstrate the usefulness of the methods developed in this thesis in real-world decision making.

Related papers

• Sample Efficient Active Algorithms for Offline Reinforcement Learning [11.11852070175351]
  offline reinforcement learning (RL) enables policy learning from static data but often suffers from poor coverage of state-action space and distributional shift problems.<n>This paper develops a rigorous sample-complexity analysis of ActiveRL through the lens of Gaussian Process (GP) uncertainty modeling.<n>Our results reveal that ActiveRL achieves near-optimal information efficiency, that is, guided uncertainty reduction leads to accelerated value-function convergence with minimal online data.
  arXiv  Detail & Related papers  (2026-02-01T14:38:07Z)
• What Matters for Batch Online Reinforcement Learning in Robotics? [65.06558240091758]
  The ability to learn from large batches of autonomously collected data for policy improvement holds the promise of enabling truly scalable robot learning.<n>Previous works have applied imitation learning and filtered imitation learning methods to the batch online RL problem.<n>We analyze how these axes affect performance and scaling with the amount of autonomous data.
  arXiv  Detail & Related papers  (2025-05-12T21:24:22Z)
• Evaluation-Time Policy Switching for Offline Reinforcement Learning [5.052293146674794]
  offline reinforcement learning (RL) looks at learning how to optimally solve tasks using a fixed dataset of interactions from the environment.<n>Many off-policy algorithms developed for online learning struggle in the offline setting as they tend to over-estimate the behaviour of out of distributions of actions.<n>Existing offline RL algorithms adapt off-policy algorithms, employing techniques such as constraining the policy or modifying the value function to achieve good performance on individual datasets.<n>We introduce a policy switching technique that dynamically combines the behaviour of a pure off-policy RL agent, for improving behaviour, and a behavioural cloning (BC) agent, for staying close to the
  arXiv  Detail & Related papers  (2025-03-15T18:12:16Z)
• RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent [11.22833419439317]
  Empirical robustness risk (ERM) is a cornerstone of modern machine learning (ML)<n>This paper focuses on the man-in-the-middle (MITM) attack, which can cause models to deviate significantly from their intended ERM solutions.<n>We propose RESIST, an algorithm designed to be robust against adversarially compromised communication links.
  arXiv  Detail & Related papers  (2025-02-11T21:48:10Z)
• Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks [71.30914500714262]
  Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate.<n>Joint subcarrier allocation and beamforming optimization are investigated for the MEC-aided cell-free network from the perspective of deep learning.
  arXiv  Detail & Related papers  (2024-12-21T10:18:55Z)
• Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness [30.44174123736964]
  We introduce an innovative adaptive fairness-aware online meta-learning algorithm, referred to as FairSAOML. Our experimental evaluation on various real-world datasets in dynamic environments demonstrates that our proposed FairSAOML algorithm consistently outperforms alternative approaches.
  arXiv  Detail & Related papers  (2024-02-19T17:44:35Z)
• RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
  We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
  arXiv  Detail & Related papers  (2023-11-21T21:05:21Z)
• A Strong Baseline for Batch Imitation Learning [25.392006064406967]
  We provide an easy-to-implement, novel algorithm for imitation learning under a strict data paradigm. This paradigm allows our algorithm to be used for environments in which safety or cost are of critical concern.
  arXiv  Detail & Related papers  (2023-02-06T14:03:33Z)
• Improving Behavioural Cloning with Positive Unlabeled Learning [15.484227081812852]
  We propose a novel iterative learning algorithm for identifying expert trajectories in mixed-quality robotics datasets. Applying behavioral cloning to the resulting filtered dataset outperforms several competitive offline reinforcement learning and imitation learning baselines.
  arXiv  Detail & Related papers  (2023-01-27T14:17:45Z)
• Reinforcement Learning with Stepwise Fairness Constraints [50.538878453547966]
  We introduce the study of reinforcement learning with stepwise fairness constraints. We provide learning algorithms with strong theoretical guarantees in regard to policy optimality and fairness violation.
  arXiv  Detail & Related papers  (2022-11-08T04:06:23Z)
• Can Active Learning Preemptively Mitigate Fairness Issues? [66.84854430781097]
  dataset bias is one of the prevailing causes of unfairness in machine learning. We study whether models trained with uncertainty-based ALs are fairer in their decisions with respect to a protected class. We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
  arXiv  Detail & Related papers  (2021-04-14T14:20:22Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.