Researchers from MIT discovered the 'indoor training effect', suggesting AI agents trained in less noisy environments may outperform those trained in similar, unpredictable settings. This counterintuitive finding indicates that training in a simulated space with lower uncertainty, as seen through studies of AI agents playing modified Atari games, can lead to greater adaptability in more complex real-world conditions. This challenges current engineering strategies that emphasize matching training and deployment environments, opening avenues for innovative training methodologies.
If we learn to play tennis in an indoor environment where there is no noise, we might be able to more easily master different shots.
This is an entirely new axis to think about. Rather than trying to match the training and testing environments, we may be able to construct simulated environments where an AI agent learns even better.
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