Agbots 2.0: Weeding Denser Fields with Fewer Robots

This work presents a significantly improved strategy for coordinated multi-agent weeding under conditions of partial environmental information. We show that by using Entropic value-at-risk (EVaR) together with the Gittins index, agents can make intelligent decisions about whether to exploit the estimated distribution of weeds in the environment or to explore new areas of the environment. The use of this method improves the performance of agents in comparison to previous methods, resulting in a system which can weed denser fields using fewer robots. Furthermore, we show that for the reward function and environmental dynamics which represent the weeding problem, our system is able to perform comparably to the fully observed case over the real-world range of seed bank densities, while operating under partial observability.