Distributed Online Learning for Multi-Stage Systems with End-to-End Bandit Feedback

Core Concepts

This paper proposes a distributed online learning algorithm called ε-EXP3 that achieves sublinear regret in multi-stage systems with end-to-end bandit feedback.

Abstract

The paper studies multi-stage systems where each job needs to go through multiple stages, each managed by a different agent. The agents can only control their own actions and learn the final outcome of the job, without knowledge or control over the actions taken by agents in the next stage. The key highlights and insights are: In addition to the exploration-exploitation dilemma in traditional multi-armed bandit problems, multi-stage systems introduce a third component - education, where an agent needs to choose actions to facilitate the learning of agents in the next stage. The paper proposes a distributed online learning algorithm called ε-EXP3 that explicitly addresses the exploration-exploitation-education trilemma. ε-EXP3 has two modes - a uniform selection mode for education and an EXP3 mode for exploration-exploitation. The paper theoretically proves that ε-EXP3 achieves sublinear regret, with the regret scaling as O(T^(L/(L+1))), where L is the depth of the system. Simulation results show that ε-EXP3 significantly outperforms existing no-regret algorithms designed for traditional multi-armed bandit problems when applied to multi-stage systems.

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Key Insights Distilled From

Distributed No-Regret Learning for Multi-Stage Systems with End-to-End Bandit Feedback

by I-Hong Hou at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04509.pdf

Deeper Inquiries

How can the ε-EXP3 algorithm be extended to handle more complex system dynamics, such as non-stationary environments or partial feedback

To extend the ε-EXP3 algorithm to handle more complex system dynamics, such as non-stationary environments or partial feedback, several modifications can be made. Non-Stationary Environments: In non-stationary environments where the underlying dynamics change over time, the algorithm can be adapted to incorporate adaptive learning rates or exploration parameters. By dynamically adjusting these parameters based on the observed feedback, the algorithm can effectively adapt to changing conditions and optimize its decision-making process. Partial Feedback: When dealing with partial feedback, where agents only receive limited information about the outcomes of their actions, the algorithm can be enhanced to incorporate reinforcement learning techniques. By leveraging reinforcement learning methods, agents can learn from both observed and unobserved feedback, allowing for more informed decision-making even with incomplete information. Exploration-Exploitation Trade-off: Balancing the exploration-exploitation trade-off becomes crucial in dynamic and partially observable environments. The algorithm can be enhanced to prioritize exploration during uncertain periods or when new information is introduced, while shifting towards exploitation as more data is gathered and the system dynamics become clearer. By incorporating these adaptations, the ε-EXP3 algorithm can be extended to effectively handle the complexities of non-stationary environments and partial feedback, ensuring robust and adaptive decision-making capabilities.

What are the practical implications and potential applications of the proposed distributed learning framework for multi-stage systems

The proposed distributed learning framework for multi-stage systems has significant practical implications and potential applications in various domains. Mobile Edge Computing: In mobile edge computing scenarios, where tasks are offloaded to edge servers with varying configurations, the framework can optimize decision-making processes to minimize latency and improve task performance. By distributing learning algorithms across multiple stages, the system can adapt to changing network conditions and user requirements efficiently. Multi-Hop Networks: In multi-hop networks, where packets are relayed through multiple routers, the framework can optimize routing decisions to minimize end-to-end latency and improve network efficiency. By enabling agents at each stage to learn and adapt their actions based on end-to-end feedback, the system can dynamically adjust routing paths for optimal performance. Resource Allocation: The framework can also be applied to resource allocation problems in distributed systems, where resources need to be allocated efficiently across multiple stages. By leveraging distributed learning algorithms, the system can optimize resource utilization, improve task completion times, and enhance overall system performance. Overall, the proposed framework offers a versatile and adaptive approach to decision-making in multi-stage systems, with applications in mobile edge computing, multi-hop networks, resource allocation, and other domains requiring distributed optimization.

How can the insights from this work be applied to other multi-agent decision-making problems beyond the specific multi-stage system setting

The insights from this work can be applied to a wide range of multi-agent decision-making problems beyond the specific multi-stage system setting. Reinforcement Learning: The principles of distributed online learning and the exploration-exploitation-education trilemma can be applied to reinforcement learning problems where multiple agents interact in complex environments. By adapting the ε-EXP3 algorithm's concepts, agents can learn to collaborate and make optimal decisions in dynamic and uncertain environments. Collaborative Robotics: In collaborative robotics, where multiple robots work together to achieve common goals, the framework can be used to optimize task allocation, coordination, and decision-making. By implementing distributed learning algorithms, robots can learn from each other's actions and improve their collective performance over time. Supply Chain Management: In supply chain management, where multiple entities make decisions on inventory, production, and distribution, the framework can enhance decision-making processes by enabling agents to learn and adapt based on end-to-end feedback. By incorporating distributed learning algorithms, supply chain systems can optimize resource allocation, reduce costs, and improve overall efficiency. By applying the insights from this work to various multi-agent decision-making problems, organizations can enhance their decision-making processes, optimize resource utilization, and achieve better outcomes in complex and dynamic environments.