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Video games have served as useful benchmarks for the decision makingcommunity, but going beyond Atari games towards training agents in modern gameshas been prohibitively expensive for the vast majority of the researchcommunity. Recent progress in the research, development and open release oflarge vision models has the potential to amortize some of these costs acrossthe community. However, it is currently unclear which of these models havelearnt representations that retain information critical for sequential decisionmaking. Towards enabling wider participation in the research of gameplayingagents in modern games, we present a systematic study of imitation learningwith publicly available visual encoders compared to the typical, task-specific,end-to-end training approach in Minecraft, Minecraft Dungeons andCounter-Strike: Global Offensive.

Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games