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Procedural Content Generation via Reinforcement Learning (PCGRL) offers a method for training controllable level designer agents without the need for human datasets, using metrics that serve as proxies for level quality as rewards. Existing PCGRL research focuses on single generator agents, but are bottlenecked by the need to frequently recalculate heuristics of level quality and the agent's need to navigate around potentially large maps. By framing level generation as a multi-agent problem, we mitigate the efficiency bottleneck of single-agent PCGRL by reducing the number of reward calculations relative to the number of agent actions. We also find that multi-agent level generators are better able to generalize to out-of-distribution map shapes, which we argue is due to the generators' learning more local, modular design policies. We conclude that treating content generation as a distributed, multi-agent task is beneficial for generating functional artifacts at scale.

In the 1930s, there were around 6.8 million farms in the United States. The size averaged at around 155 acres. Over the next several decades, the number of farms rapidly declined to around 1.9 million in 2023. Those farms grew larger, averaging around 464 acres. As farming has changed over time, experts believe artificial intelligence (AI) can help farmers and producers make food faster and more efficiently.

Awareness of artificial intelligence (AI) is increasing throughout health care. Relative to pharmacy, the American Society of Health-Systems Pharmacists Foundation's "Pharmacy Forecast 2020: Strategic Planning Advice for Pharmacy Departments in Hospitals and Health Systems" specifically lists the emergence of AI in its report, inspiring industry discussion and guiding the strategic planning processes of health system pharmacy leadership across the country.1 For pharmacy, AI provides information on drug interactions, drug therapy monitoring, formulary selection, costs, usage trends, and more. "AI is already transforming health care but will become increasingly valuable as investments in systems that can capture and manage data are made and clinical informatics entities work more collaboratively to address current data shortfalls," said Doug Zurawski, PharmD, senior vice president of clinical strategy at Kit Check, Inc, maker of radio frequency identification (RFID)/AI technology for hospital pharmacies to help with medication management. "It is incumbent upon us, in this industry and in this field, to take the lead and learn more, invest in systems that support AI and machine learning, and prepare for the future with access to artificial intelligence."