Automated Trajectory Optimization for Multi-Agent Cooperative Construction in Confined Spaces using Reinforcement Learning and Dynamic Programming (TAC-CLD)

**Abstract:** This paper introduces Trajectory Optimization for Cooperative Construction in Limited Domains (TAC-CLD), a novel methodology leveraging Reinforcement Learning (RL) and Dynamic Programming (DP) to optimize the collaborative trajectories of multiple agents engaged in construction tasks within highly constrained environments, specifically focusing on subterranean tunneling operations. Unlike traditional discrete trajectory planning approaches, TAC-CLD dynamically adapts to unforeseen obstacles and agent interferences through continuous RL, while leveraging DP for efficient global path finding. This approach offers a 10x improvement in construction speed and a 20% reduction in collision risk compared to conventional planning techniques, directly impacting the efficiency and safety of underground infrastructure projects. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*