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New training approach could help AI agents perform better in uncertain conditions
AI agents trained in simulations that differ from the environments where they are deployed sometimes perform better than agents trained and deployed in the same environment, research shows.
The new training method seems like a significant step forward for AI agents, especially when dealing with unpredictable environments. However, it's crucial to consider how these agents will adapt to different types of uncertainties in real-world scenarios. Have there been any tests conducted to evaluate how well these agents perform in various conditions?