arxiv_ai 95% Match Research Paper RL Researchers,Robotics Engineers,AI Safety Researchers,Data Scientists 1 week ago

Online Optimization for Offline Safe Reinforcement Learning

reinforcement-learning › offline-rl

📄 Abstract

Abstract: We study the problem of Offline Safe Reinforcement Learning (OSRL), where the goal is to learn a reward-maximizing policy from fixed data under a cumulative cost constraint. We propose a novel OSRL approach that frames the problem as a minimax objective and solves it by combining offline RL with online optimization algorithms. We prove the approximate optimality of this approach when integrated with an approximate offline RL oracle and no-regret online optimization. We also present a practical approximation that can be combined with any offline RL algorithm, eliminating the need for offline policy evaluation. Empirical results on the DSRL benchmark demonstrate that our method reliably enforces safety constraints under stringent cost budgets, while achieving high rewards. The code is available at https://github.com/yassineCh/O3SRL.

Authors (5)

Yassine Chemingui

Aryan Deshwal

Alan Fern

Thanh Nguyen-Tang

Janardhan Rao Doppa

Submitted

October 24, 2025

arXiv Category

cs.LG

arXiv PDF Code

Key Contributions

This paper proposes a novel approach for Offline Safe Reinforcement Learning (OSRL) by framing it as a minimax objective solved via online optimization. It proves approximate optimality and offers a practical approximation that bypasses the need for offline policy evaluation, demonstrating reliable safety constraint enforcement and high rewards on the DSRL benchmark.

Business Value

Enables the development of safer autonomous systems and decision-making agents by learning from existing data without requiring online interaction, crucial for high-stakes applications like autonomous driving or medical treatment planning.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

High, as it leverages existing data and avoids complex online interaction requirements, making it suitable for scenarios where real-world exploration is costly or dangerous.

Limitations Addressed

Challenges in offline RL with safety constraints, particularly the need for offline policy evaluation and ensuring reliable constraint adherence.

Performance Gains

Achieves high rewards while reliably enforcing safety constraints under stringent cost budgets.

View Code on GitHub

Technical Tags

Offline Reinforcement LearningSafe RLOnline OptimizationMinimax ObjectiveCumulative Cost ConstraintPolicy EvaluationDSRL Benchmark

Research Topics

Safe Reinforcement LearningOffline RL with Safety ConstraintsOnline Optimization for RLLearning from Fixed Datasets

Methods & Architectures

Framing OSRL as a minimax objectiveCombining offline RL with online optimization algorithmsApproximate offline RL oracleNo-regret online optimizationPractical approximation eliminating offline policy evaluation

Applications & Tasks

Robotics Autonomous Systems Healthcare Finance Learning policies from fixed datasets under safety constraintsOptimizing reward while adhering to cost budgets Offline Safe Reinforcement LearningLearning safe policies from historical data

Datasets & Benchmarks

Datasets

DSRL benchmark

Benchmarks

DSRL benchmark: reliably enforces safety constraints under stringent cost budgets, while achieving high rewards.

RewardSafety constraintsCost budgets

Related Fields

Reinforcement LearningControl TheoryOptimizationMachine Learning Safety

Keywords

Offline RLSafe RLOnline OptimizationMinimaxCost ConstraintPolicy LearningDSRLAutonomous SystemsDecision MakingData Efficiency

Academic Context

#Safe Reinforcement Learning#Offline RL with Safety Constraints#Online Optimization for RL#Learning from Fixed Datasets

Commercial Potential

Potential Products

Safe autonomous driving systemsRobotic control systems with safety guaranteesPersonalized treatment recommendation systems

Target Industries

AutomotiveRoboticsHealthcareFinanceAerospace

Use Case Examples

Training a robot arm to perform a task without damaging itself or its environment using historical operational data.Developing a recommendation system that avoids suggesting treatments with high risk of side effects based on past patient data.

Competitive Edge

Addresses the critical need for safety in offline RL, offering a method that is both theoretically grounded and practically applicable without requiring expensive online policy evaluation.

Market Opportunity

Growing market for AI safety and reliable autonomous systems.

Revenue Models

Licensing of algorithmsconsulting services for implementationdevelopment of specialized safe RL agents.

Resource Requirements

Compute Needs

Moderate to high, depending on the complexity of the offline RL problem and the size of the dataset.

Data Requirements

Requires a fixed dataset of state-action-reward-next_state transitions, potentially with associated costs or safety labels.

Deployment Constraints

Quality and coverage of the offline dataset are crucial.,Ensuring the learned policy adheres strictly to safety constraints in real-world deployment.

Scalability

Scalable to large datasets and complex state-action spaces, leveraging efficient online optimization techniques.

Regulatory Considerations

Safety regulations in critical domains (e.g., autonomous vehicles, healthcare).

Production Readiness

Maturity Level

Research

Time to Market

1-3 years for specific applications, depending on domain validation.

Licensing

Code available under a permissive license (implied by GitHub availability).

View Full Paper Back to Papers