A Reduction-based Framework for Sequential Decision Making with Delayed Feedback

Core Concepts

The author proposes a reduction-based framework to handle stochastic delays in sequential decision-making problems, converting multi-batched algorithms into sample-efficient solutions. This framework provides sharper regret bounds and addresses delays in both single-agent and multi-agent settings.

Abstract

The content discusses the challenges of delayed feedback in decision-making scenarios, proposing a novel reduction-based framework to address these issues. By converting multi-batched algorithms, the framework offers efficient solutions for handling stochastic delays in various decision-making problems.
The paper covers various aspects of sequential decision making, including bandits, Markov decision processes (MDPs), and Markov games (MGs). It introduces a new framework that enhances existing results and provides a comprehensive set of sharp results for single-agent and multi-agent sequential decision-making problems with delayed feedback.
Key points include:

Introduction to delayed feedback as a common challenge in sequential decision making.
Proposal of a reduction-based framework to handle stochastic delays efficiently.
Application of the framework to various decision-making scenarios such as bandits, MDPs, and MGs.
Demonstration of improved regret bounds and new results using the proposed approach.
Overall, the content highlights the significance of addressing delayed feedback in decision-making processes through innovative frameworks and algorithms.

Stats

O(A log K)
O(√dK + d3/2E[τ])
O(√H3SAK + H2SAE[τ] log K)
O(√d3H4K + dH2E[τ])
O(H3√SAmax + H3pSE[τ]K)

Quotes

"We propose a novel reduction-based framework."
"Our contributions can be summarized as follows."
"The proposed framework converts any multi-batched algorithm into an efficient solution."

Key Insights Distilled From

A Reduction-based Framework for Sequential Decision Making with Delayed Feedback

by Yunchang Yan... at arxiv.org 03-07-2024

https://arxiv.org/pdf/2302.01477.pdf

Deeper Inquiries

How does the proposed reduction-based framework compare to existing approaches

The proposed reduction-based framework in the paper offers a novel approach to handling delayed feedback in sequential decision-making problems. Unlike existing approaches that require case-by-case algorithm design and analysis, this framework provides a unified solution for a wide range of problems. By converting any multi-batched algorithm into an algorithm capable of handling stochastic delays, the framework simplifies the process of adapting existing algorithms to account for delays. This not only streamlines the development process but also ensures sample-efficient solutions for sequential decision-making with delayed feedback.

What are the potential implications of this research on real-world applications

The research presented in this paper has significant implications for real-world applications where delayed feedback is common. For instance, in recommendation systems, robotics, or video streaming services, delays in receiving feedback are inherent and can significantly impact decision-making processes. By providing a comprehensive set of results for single-agent and multi-agent sequential decision-making problems with delayed feedback, this research opens up new possibilities for developing more efficient algorithms that can handle stochastic delays effectively. Implementing these findings could lead to improved performance and better outcomes in various practical scenarios.

How might delays impact decision-making processes beyond what is discussed in this paper

Delays can have far-reaching impacts on decision-making processes beyond what is discussed in the paper. In complex systems such as financial trading or autonomous vehicles, even small delays can result in suboptimal decisions or potentially dangerous situations. Understanding how delays affect different types of decision-making algorithms is crucial for ensuring robustness and reliability in real-world applications. Additionally, exploring the effects of delays on coordination among multiple agents or entities could shed light on optimizing collaborative strategies under uncertain conditions. Overall, considering delay factors is essential for designing resilient and adaptive systems across various domains.