Transfer in Sequential Multi-armed Bandits via Reward Samples Analysis

Core Concepts

Proposing an algorithm for transferring reward samples in sequential multi-armed bandit problems to improve cumulative regret performance.

Abstract

The content discusses the application of transfer learning in sequential stochastic multi-armed bandit problems. It introduces an algorithm based on UCB to transfer reward samples from previous episodes, aiming to enhance overall performance. The paper provides regret analysis and empirical results showing significant improvement over standard UCB without transfer. I. Introduction Multi-armed Bandit (MAB) problem overview. Application of MAB in online advertisements and recommender systems. Importance of transfer learning in MAB problems. II. Preliminaries and Problem Statement Definition of the Multi-Armed Bandit problem with K arms and J episodes. Assumption regarding the relatedness of mean rewards across episodes. Goal of the agent to maximize average reward over all episodes. III. All Sample Transfer UCB (AST-UCB) Description of the UCB Algorithm. Introduction to the AST-UCB Algorithm for transferring knowledge using reward samples from previous episodes. IV. Regret Analysis Derivation of regret bounds for the AST-UCB algorithm. V. Numerical Simulations Presentation of numerical results comparing AST-UCB and NT-UCB algorithms. VI. Conclusion Concluding remarks on the proposed transfer algorithm's effectiveness and suggestions for future research directions.

Stats

1{E} denotes the indicator function whose value is 1 if the event (condition) E is true, and 0 otherwise. ∅ denotes null set. arXiv:2403.12428v1 [cs.LG] 19 Mar 2024

Quotes

"We propose an algorithm based on UCB to transfer knowledge using reward samples from previous episodes." "Our algorithm has a better performance compared to UCB with no transfer."

Key Insights Distilled From

Transfer in Sequential Multi-armed Bandits via Reward Samples

by Rahul N R,Va... at arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12428.pdf

Deeper Inquiries

How can this approach be extended to scenarios where the parameter ϵ is unknown

In scenarios where the parameter ϵ is unknown, one approach to extend this transfer learning method would be to incorporate an adaptive mechanism to estimate or update the value of ϵ during the learning process. This adaptation could be based on the observed rewards and their variations across episodes. By dynamically adjusting ϵ, the algorithm can continuously refine its understanding of how closely related different bandit problems are in terms of mean reward distributions. Techniques like online estimation or Bayesian updating could be employed to iteratively learn and adapt ϵ as more data becomes available.

What are potential drawbacks or limitations of transferring knowledge between different bandit problems

Transferring knowledge between different bandit problems may face several drawbacks or limitations: Negative Transfer: In some cases, transferring knowledge from one problem to another may not always lead to performance improvement. The assumptions underlying the transfer might not hold true for all scenarios, leading to negative impacts on decision-making. Overfitting: If there is a significant mismatch between the source and target bandit problems, transferring knowledge without proper adjustments can result in overfitting. The transferred information might not generalize well across diverse environments. Complexity: Implementing transfer learning techniques adds complexity to the model and requires careful tuning of hyperparameters such as ϵ. Managing these additional parameters effectively can be challenging. Computational Overhead: Transferring large amounts of data or samples between episodes can introduce computational overhead, especially in real-time applications where decisions need to be made quickly. Limited Generalization: The effectiveness of knowledge transfer heavily relies on how similar or related the bandit problems are in practice. Limited generalization capabilities may restrict the applicability of transferred knowledge beyond specific contexts.

How might this research impact other areas beyond machine learning applications

This research on transferring reward samples in sequential multi-armed bandits has implications beyond machine learning applications: Decision-Making Systems: The concept of leveraging past experiences from related tasks can benefit decision-making systems across various domains such as finance, healthcare, autonomous vehicles, and resource allocation by improving efficiency and reducing exploration time. 2Personalized Recommendations:: In recommendation systems like e-commerce platforms or content streaming services, applying transfer learning techniques could enhance user experience by adapting recommendations based on historical user interactions with similar items. 3Dynamic Resource Allocation:: Industries dealing with dynamic resource allocation challenges (e.g., energy management) could utilize these methods for optimizing resource utilization over changing conditions while minimizing regret. 4Adaptive Learning Algorithms:: Insights gained from this research could inspire new approaches for developing adaptive algorithms that adjust their strategies based on evolving environments or tasks without starting from scratch each time.