[Paper Review] Distributed Learning: Sequential Decision Making in Resource-Constrained Environments

Udari Madhushani, Naomi Ehrich Leonard|arXiv (Cornell University)|Apr 13, 2020

Advanced Bandit Algorithms Research18 references4 citations

TL;DR

This paper proposes a partial communication protocol for distributed multi-armed bandit learning that transmits information only during exploration actions, achieving the same performance order as full communication while reducing communication cost to O(log T). The protocol enables cost-effective, scalable learning in resource-constrained environments by minimizing data transmission without sacrificing group performance.

ABSTRACT

We study cost-effective communication strategies that can be used to improve the performance of distributed learning systems in resource-constrained environments. For distributed learning in sequential decision making, we propose a new cost-effective partial communication protocol. We illustrate that with this protocol the group obtains the same order of performance that it obtains with full communication. Moreover, we prove that under the proposed partial communication protocol the communication cost is $O(\log T)$, where $T$ is the time horizon of the decision-making process. This improves significantly on protocols with full communication, which incur a communication cost that is $O(T)$. We validate our theoretical results using numerical simulations.

Motivation & Objective

• To develop cost-efficient communication strategies for distributed learning in resource-constrained environments where full communication is impractical.
• To reduce communication overhead in sequential decision-making systems without degrading group performance.
• To design a partial communication protocol that maintains performance equivalent to full communication while minimizing data transmission.
• To analyze and validate a protocol that shares information only during exploration actions, leveraging exploit-based and explore-based communication distinctions.
• To demonstrate that explore-based communication yields the same performance order as full communication with drastically reduced communication cost.

Proposed method

• Proposes a partial communication protocol that restricts information sharing to only when agents perform exploring actions, defined as actions on sub-optimal or uncertain options.
• Uses the multi-armed bandit framework to model sequential decision-making under stochastic rewards, with agents selecting actions to maximize cumulative reward.
• Defines communication cost as the total number of messages exchanged, with full communication incurring O(T) cost and the proposed protocol achieving O(log T) expected cost.
• Introduces a sampling rule (Definition 1) that guides agents in balancing exploration and exploitation, with communication triggered only during exploration.
• Employs expected cumulative regret as the performance metric, where minimizing regret corresponds to maximizing group reward.
• Validates the protocol using numerical simulations with 100 agents, 10 options, and 1000 time steps under Gaussian reward distributions and a complete communication graph.

Experimental results

Research questions

• RQ1 Can a partial communication protocol achieve the same performance order as full communication while significantly reducing communication cost in distributed learning?
• RQ2 What is the communication cost of a protocol that shares information only during exploration actions, and how does it scale with time horizon T?
• RQ3 How does explore-based communication compare to full communication and exploit-based communication in terms of both performance and communication cost?
• RQ4 Does limiting communication to exploration actions preserve group performance while reducing data transmission in sequential decision-making systems?
• RQ5 Can a communication protocol that relies solely on explore-based sharing maintain high performance in resource-constrained, distributed bandit settings?

Key findings

• The proposed explore-based communication protocol achieves the same order of performance as full communication, with expected cumulative regret remaining bounded and comparable to the full-communication case.
• The expected communication cost under the proposed protocol is O(log T), a significant improvement over the O(T) cost of full communication.
• Numerical simulations show that explore-based communication reduces communication cost per agent by orders of magnitude compared to full communication, while maintaining near-identical performance.
• Exploit-based communication and no communication result in significantly worse performance, with exploit-based communication incurring communication costs close to full communication.
• The protocol demonstrates that sharing only during exploration is sufficient to achieve high group performance, validating the theoretical cost-performance trade-off.
• The results confirm that communication during exploration alone enables efficient information sharing, making the protocol practical for long-horizon, resource-constrained learning systems.