一种基于模型强化学习的模块化生产系统分布式优化方法
Distributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models
这是篇挺有意思的论文，用模型强化学习来优化模块化生产系统，看起来能提升效率。
这篇论文提出了一种用于高度灵活、模块化制造系统的数据驱动自学习控制的新方法。具体来说，它采用了一种新型基于模型的强化学习框架，在训练强化策略时引入了近似逆过程模型。这种方法将操作动力学和状态空间中的动力学学习解耦，使得强化学习仅在任务空间内进行训练。他们提出了一种轻量级的正向传播架构用于近似逆模型，并将其集成到标准强化学习算法的策略网络中。他们将该方法应用于一个具有异构生产模块的实验室模块化生产测试平台。结果表明，该方法在性能和训练速度方面显著提高了模块化制造单元的效率，尤其对离线算法而言。
Distributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models
This paper presents a novel approach for data-driven self-learning control of highly flexible, modular manufacturing systems. Specifically, we employ a novel framework for model-based reinforcement learning which introduces approximate inverse process models within the training of reinforcement policies. This approach disentangles the learning of actuation dynamics and the dynamics in state space, resulting in RL-based training solely within the task space. We propose a lightweight feedforward architecture for approximate inverse models and integrate them within the policy network of standard RL algorithms. We apply the approach to a laboratory modular production testbed with heterogeneous production modules. The results underline the efficiency improvements for modular manufacturing units in terms of both performance and training speed, particularly for off-policy algorithms.