Letting robots emulate human behavior has always posed a challenge, particularly in scenarios involving multiple robots. In this paper, we presented a framework aimed at achieving multi-agent reinforcement learning for robot control in construction tasks. The construction industry often necessitates complex interactions and coordination among multiple robots, demanding a solution that enables effective collaboration and efficient task execution. Our proposed framework leverages the principles of proximal policy optimization and developed a multi-agent version to enable the robots to acquire sophisticated control policies. We evaluated the effectiveness of our framework by learning four different collaborative tasks in the construction environments. The results demonstrated the capability of our approach in enabling multiple robots to learn and adapt their behaviors in complex construction tasks while effectively preventing collisions. Results also revealed the potential of combining and exploring the advantages of reinforcement learning algorithms and inverse kinematics. The findings from this research contributed to the advancement of multi-agent reinforcement learning in the domain of construction robotics. By enabling robots to behave like human counterparts and collaborate effectively, we pave the way for more efficient, flexible, and intelligent construction processes.

翻译：让机器人模仿人类行为始终是一项挑战，尤其在涉及多机器人协作的场景中。本文提出了一种框架，旨在实现建筑施工任务中机器人控制的多智能体强化学习。建筑行业通常需要多机器人间复杂的交互与协调，这要求解决方案能够支持有效协作与高效任务执行。本框架利用近端策略优化原理，开发了多智能体版本，使机器人能够习得复杂控制策略。通过在学习建筑环境中的四种不同协作任务进行验证，结果显示该方法能使多机器人学习并适应复杂施工行为，同时有效避免碰撞。结果还揭示了结合并探索强化学习算法与逆运动学优势的潜力。本研究推动了多智能体强化学习在建筑机器人领域的进展。通过使机器人具备类似人类的行为模式并实现高效协作，为构建更高效、灵活与智能的施工流程奠定了基础。