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

• URL: http://arxiv.org/abs/2312.02312v1
• Date: Mon, 4 Dec 2023 19:52:12 GMT
• Title: Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games
• Authors: Lukas Sch\"afer, Logan Jones, Anssi Kanervisto, Yuhan Cao, Tabish Rashid, Raluca Georgescu, Dave Bignell, Siddhartha Sen, Andrea Trevi\~no Gavito, Sam Devlin
• Abstract summary: Going beyond Atari games towards training agents in modern games has been prohibitively expensive for the vast majority of the research community. Recent progress in the research, development and open release of large vision models has the potential to amortize some of these costs across the community. We present a systematic study of imitation learning with publicly available visual encoders compared to the typical, task-specific, end-to-end training approach in Minecraft, Minecraft Dungeons and Counter-Strike: Global Offensive.
• Score: 13.241655571625822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video games have served as useful benchmarks for the decision making community, but going beyond Atari games towards training agents in modern games has been prohibitively expensive for the vast majority of the research community. Recent progress in the research, development and open release of large vision models has the potential to amortize some of these costs across the community. However, it is currently unclear which of these models have learnt representations that retain information critical for sequential decision making. Towards enabling wider participation in the research of gameplaying agents in modern games, we present a systematic study of imitation learning with publicly available visual encoders compared to the typical, task-specific, end-to-end training approach in Minecraft, Minecraft Dungeons and Counter-Strike: Global Offensive.

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