Bridge the Generalization Gap: A Comprehensive Framework for Enhance Reliability and Robustness in Deep Learning Models

Deep reinforcement learning (DRL) has been successful in a wide range of domains but its broad acceptance is limited by the generalization gap, which measures the performance difference between an environment a model was trained with versus new, unseen environments. This gap implies a challenge that needs to be addressed for DRL models used in real-world adaptation. This paper considers a strategy whose methodology outlines combining domain randomization, multitask learning, and regularization techniques. The study trains the models on multiple tasks and diverse environments, thus generalizing the ability of models. Our regularization strategy keeps the model from over-specialization to the training data and ensures improved performance. Benchmark environments are evaluated based on our approach, reducing the generalization gap with improvement in average returns in unseen environments and increasing the resilience of DRL models. In this regard, our study opens up the avenue for a significant contribution to the generalization capacity of more robust DRL models for real-world applicability.