Diversity in multi-agent systems for successful real-world deployments

机器学习是一种数据驱动型人工智能，已经给多个行业带来了革命性的变化。这不仅可以实现更高的性能，还可以带来以前无法实现的机会。不幸的是，学习可能会非常缓慢，特别是当数据收集成本很高或收集速度很慢的时候。例如，与其让机器人在现实世界中学习几天，这既耗时又费钱，为什么不先在模拟中学习，然后转移到现实世界中呢？不幸的是，因为模拟从来都不是完美的，所以需要某种类型的适应和模拟更改策略，通常称为Sim2Real。虽然在单代理设置中很常见，但多代理Sim2Real(MAS2R)尚未被研究，尽管它是许多现实世界潜在突破的关键组件。该项目将扩展和测试MAS2R，不仅考虑到情况变化，还考虑到环境中有不同类型的参与者。 MAS2R将应用于以下方面的高保真模拟： 自动驾驶 智能电网 智能建筑 将探索商业化选择，确定一条或多条商业化道路，以帮助艾伯塔省的公司在获奖后采取下一步行动。

Machine learning, a data-driven type of artificial intelligence, has revolutionized multiple industries. Not only does this allow for higher performance but it can also enable previously unrealizable opportunities. Unfortunately, learning can be impractically slow, particularly when data is expensive or slow to gather. For example, rather than letting a robot learn for days in the real world, which costs both time and money, why not first learn in simulation and then transfer to the real world? Unfortunately, because simulations are never perfect, some type of adaptation and simulation change strategies are needed, typically called Sim2Real. While common in single-agent settings, Multi-agent Sim2Real (MAS2R), has not been studied, even though it is a critical component to many real-world potential breakthroughs. This project will extend and test MAS2R, considering not only when situations change but also when different types of actors are in the environment. MAS2R will be applied to high-fidelity simulations in: Autonomous Driving Smart Grid Smart Buildings Commercialization options will be explored, with one or more paths forward towards commercialization decided on for next steps to help Alberta companies, post-award.