Multi-agent simulations are some of the most complex for a simulation modeler to optimize. Most optimization solutions are suited for a single agent or a small number of agents, but quickly lose their effectiveness as more agents are introduced.
Simulations of more complex operations, such as fleets of autonomous vehicles, or complicated production-line equipment networks, need an optimization tool that is capable of making fast decisions while coordinating the work of many agents.
Pathmind reinforcement learning offers an optimal solution capable of handling multi-agent models. These models can see dramatic performance improvements using Pathmind’s AI, which learns the emergent behavior necessary to reach goals in simulations with many agents.
A simulation consultancy worked with Pathmind to apply reinforcement learning to intelligently re-route haul trucks at an open-pit mining site. The mining optimization project discovered a solution that resulted in a 19% increase in ore preparation.
Read the full case study here.
Optimization methods for multi-agent models are useful in many industrial contexts. Pathmind reinforcement learning is better suited for these applications and offers new insights into profitability and efficiency, while bringing AI to exciting new places.
Contact us to learn more.