These images were evenly split between cases diagnosed with adenocarcinoma of the lung and squamous cell carcinoma, representing the two most common sub-types in lung cancer. The images were scanned on an Aperio scanner at a resolution of 0 . Different classes used for conditioning were annotated digitally by a pathologist using an apple pencil with the instruction to clearly demarcate boundaries between tissue regions. The pathologist could choose from a list of 40 distinct annotation categories, aiming to cover all possible annotation requirements. All data handling was performed in strict accordance with privacy regulations and ethical standards, ensuring the protection of patient information at all times.

"Nonstationarity" is a fundamental problem in cooperative multi-agent reinforcement learning (MARL)--each agent must relearn information about the other agent's policies due to the other agents learning, causing information to "ring" between agents and convergence to be slow. The MAILP model, introduced by Terry and Grammel (2020), is a novel model of information transfer during multi-agent learning. We use the MAILP model to show that increasing training centralization arbitrarily mitigates the slowing of convergence due to nonstationarity. The most centralized case of learning is parameter sharing, an uncommonly used MARL method, specific to environments with homogeneous agents, that bootstraps a single-agent reinforcement learning (RL) methods and learns an identical policy for each agent. We experimentally replicate the result of increased learning centralization leading to better performance on the MARL benchmark set from Gupta et al. (2017). We further apply parameter sharing to 8 "more modern" single-agent deep RL (DRL) methods for the first time in the literature. With this, we achieved the best documented performance on a set of MARL benchmarks and achieved up to 44 times more average reward in as little as 16% as many episodes compared to documented parameter sharing arrangement. We finally offer a formal proof of a set of methods that allow parameter sharing to serve in environments with heterogeneous agents.