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Can we build a generalist agent? Dr. Minqi Jiang and Dr. Marc Rigter
Machine Learning Street Talk (MLST)
**Maximizing robustness through mini max regret objective training **
In reinforcement learning, optimizing for the expectation of reward may not always be the best objective. Rather than solely focusing on expected performance, a more robust approach is to aim for consistent performance across various scenarios. This can be achieved through a robust objective defined by mini max regret, which focuses on minimizing the maximum sub-optimality across all situations. Contrary to the traditional approach of maximizing performance under the most adversarial conditions, the mini max regret objective ensures that the agent performs reasonably well in every possible situation, avoiding scenarios where it is impossible to achieve any reward. This approach is advocated by researchers such as Minxi and Michael Dennis for building general agents that are resilient and robust.
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