Generalization in Safety-Critical RL
Summary
Generalization in safety-critical reinforcement learning (RL) addresses the challenge of ensuring AI agents can perform safely in novel situations after training on a limited number of environments. Research has shown that RL algorithms can fail dangerously in unseen test environments, even when performing well in training scenarios. Approaches to mitigate this issue include ensemble model averaging, blocking classifiers, and uncertainty quantification. While these methods have shown promise in simpler environments like gridworlds, they may be less effective in more complex scenarios. However, ensemble-based uncertainty information can still be valuable for predicting imminent catastrophes and determining when human intervention is necessary. This area of research is crucial for developing robust and safe AI systems that can be deployed in real-world applications with confidence.