Online Policy Customization

Online policy customization focuses on adapting pre-trained policies to new, unforeseen situations or requirements without extensive retraining. Current research emphasizes developing efficient algorithms, such as those based on residual learning and actor-critic methods, that can quickly adjust policies using minimal additional data, even leveraging programmatic representations for improved interpretability. This field is crucial for deploying robust AI systems in real-world settings, enabling personalized interventions and improving the adaptability of reinforcement learning agents across diverse tasks and environments.