Product Concept Development and Evaluation Using Multiagent Reinforcement Learning
提出一种多智能体强化学习方法，让多个智能体在共享设计环境中协作，自动管理设计数据和知识，用于产品概念开发与评估，并通过康复设备设计案例验证有效性。
Product concept development is an iterative and time-consuming task. A wide range of solutions must be developed and evaluated for the optimal result. Current methods in product concept development rely on experience of designers to explore different solutions. Reinforcement learning is a machine learning paradigm where an agent learns to make sequential decisions by interacting with the environment, receiving rewards or penalties in return for its actions. An automatic approach is introduced in this paper to manage design data and knowledge in using reinforcement learning for product concept development and evaluation. A multi-agent reinforcement learning method is proposed to enable different agents working and learning together in a shared design environment. The environment is formed by the design data and knowledge based on Quality Function Deployment and Axiomatic Design for different agents to achieve the same objective collaboratively. The proposed method improves functionality, efficiency, and user experience of the design process in product concept development. A case study of designing a rehabilitation device verifies the effectiveness of the proposed approach.