Optimizing Resource Allocation in Agile Teams Using Reinforcement Learning

One of the challenges of agile software development is resource distribution within a team, which significantly drives project success and team efficiency. This paper, therefore, proposes a holistic implementation study that utilizes reinforcement learning methods for improving decision-making on resource distribution in agile environments. The approach offsets the dynamic nature of sprint planning, task allocation, coordination issues across teams, using multi-agent deep reinforcement learning. By analyzing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{6, 4 5 0}$</tex> sprint records based on 14 essential performance indicators, this research illustrates how resource allocation based on RL has enhanced team performance metrics by utilizing resources effectively. The framework proposed uses a Deep Q-Network model that predicts the best distribution of resources, leveraging historical sprint data. The current paper presents an evidence-based framework to enhance the efficiency of resources, hence fostering the meeting point of AI and Agile project management.