Lagrangian Index Policy for Restless Bandits

The Lagrangian Index Policy (LIP) is a heuristic approach used in the field of reinforcement learning, particularly for solving restless multi-armed bandit problems. These problems involve making decisions over time to maximize rewards, where each decision affects the state of the system. The LIP is compared to the Whittle Index Policy (WIP), another heuristic known for its asymptotic optimality under certain conditions. The study highlights that while both policies perform similarly in most cases, LIP outperforms WIP in scenarios where WIP struggles. This is particularly relevant in applications like web crawling and minimizing the age of information, where timely and efficient decision-making is crucial. The research also explores reinforcement learning algorithms to implement LIP in a model-free setting, reducing memory requirements compared to WIP. This makes LIP a promising approach for resource-constrained environments, offering a balance between performance and computational efficiency.

Category: Reinforcement Learning
Subcategory: Multi-Armed Bandits
Tags: reinforcement learningLagrangian index policyrestless banditsdecision-making
AI Type: Reinforcement Learning
Programming Languages: Not specified
Frameworks/Libraries: Not specified
Application Areas: Web crawlingInformation age minimization
Manufacturer Company: Not specified
Country: Not specified
Algorithms Used

Lagrangian Index Policy, Whittle Index Policy

Model Architecture

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Datasets Used

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Performance Metrics

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Deployment Options

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Cloud Based

No

On Premises

No

Features

Efficient decision-making, reduced memory requirements

Enterprise

No

Hardware Requirements

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Open Source

No

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Inference Latency

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Energy Efficiency

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Ethical Considerations

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Industry Verticals

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Use Cases

Restless bandit problems, Decision-making

Customer Base

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SLA

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Multi-Language Support

No

Localization

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Trial Availability

No

Partner Ecosystem

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Patent Information

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Regulatory Compliance

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Version

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Website URL

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Has API

No

API Details

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Price

0.00

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License Type

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Release Date

01/01/1970

Last Update Date

01/01/1970

Contact Email

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Social Media Links

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Other Features

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Published

Yes