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=== Pure competition settings ===

When two agents are playing a [[zero-sum game]], they are in pure competition with each other. Many traditional games such as [[chess]] and [[Go (game)|Go]] fall under this category, as do two-player variants of video games like [[StarCraft]]. Because each agent can only win at the expense of the other agent, many complexities are stripped away. There is no prospect of communication or social dilemmas, as neither agent is incentivized to take actions that benefit its opponent.

The [[Deep Blue (chess computer)|Deep Blue]]<ref>{{Cite journal |last1=Campbell |first1=Murray |last2=Hoane |first2=A. Joseph Jr. |last3=Hsu |first3=Feng-hsiung |year=2002 |title=Deep Blue |journal=Artificial Intelligence |volume=134 |issue=1–2 |pages=57–83 |doi=10.1016/S0004-3702(01)00129-1 |issn=0004-3702 |publisher=Elsevier}}</ref> and [[AlphaGo]] projects demonstrate how to optimize the performance of agents in pure competition settings.

One complexity that is not stripped away in pure competition settings is [[#Autocurricula|autocurricula]]. As the agents' policy is improved using [[Self-play (reinforcement learning technique)|self-play]], multiple layers of learning may occur.