論文

Training Reinforcement Learning Policy in AnyLogic Simulation Environment Using Pathmind


In this paper, the researchers study the operations of an imaginary coffee shop with a focus on the barista’s actions. They also show how the sequence of actions affects the overall performance of the coffee shop by using reinforcement learning and simulation as its policy training environment. This model acts as a guiding example that shows the ease of applying RL in AnyLogic models using the Pathmind Library.

Business Process Modeling and Efficiency Improvement through an Agent-Based Approach


This paper describes the results of a practical experience of business process improvement and change. the business modeling approach, carried out through an agent-based model, has been applied to an operational process with the aim to reduce the overlapping of the operational phases and to improve the time-efficiency. Simulation outcomes and results are discussed.