Using reinforcement algorithms to improve the collaboration efficiency of entrepreneurial teams
Jieqiong Wang, Linghong Jiang
TL;DR
This paper explores using reinforcement learning to enhance the efficiency of entrepreneurial teams in dynamic environments.
Contribution
It introduces a MARL-based approach to improve collaboration, resource use, and task allocation in entrepreneurial teams.
Findings
MARL improved resource utilization and communication efficiency in entrepreneurial teams.
PPO-based agents achieved higher task completion rates in dynamic scenarios.
The method showed adaptability across software development, manufacturing, and logistics cases.
Abstract
Entrepreneurial Team (ET) plays an essential role in the business process by driving innovation and optimizing ideas via adaptability, collaboration, and resourcefulness. The team performance is continuously affected because of resource imbalance, poor communication and inefficient task allocation. The importance of ET in organization growth is the main reason for this analysis. Therefore, this work uses Multi-Agent Reinforcement Learning (MARL) to handle efficient dynamic decisions and coordination to improve ET efficiency in dynamic and complex environments. The main intention of this work is to improve resource utilization, communication efficiency and optimize task allocation. During the analysis, Proximal Policy Optimization (PPO) is utilized to direct agents toward achieving collaborative goals. In every state, the agent receives rewards and penalties for their actions, which…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Open Source Software Innovations · Advanced Multi-Objective Optimization Algorithms