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9min chapter
Can we build a generalist agent? Dr. Minqi Jiang and Dr. Marc Rigter
Machine Learning Street Talk (MLST)
Robust Decision Making in Adversarial Environments
The chapter explores the concept of mini-max regret in decision theory as a strategy to achieve robustness, contrasting it with the traditional approach of maximizing performance in adversarial scenarios. It emphasizes the importance of minimizing regret across various situations for a general agent. The discussion also covers reward-free exploration in model-based reinforcement learning and the benefits of explicitly modeling the environment in training and decision-making.
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