Reinforcement Learning (RL) tackles complex decision-making by training computational models through interactions. However, direct training in real-world settings—such as autonomous driving or medical procedures—is often impractical due to the high risk of costly or dangerous errors. As a result, RL commonly relies on simulated environments or static offline datasets. This
reliance, however, introduces a critical challenge known as the "reality gap"—a discrepancy between training conditions and the dynamics encountered in real-world applications. This presentation addresses innovative strategies designed to bridge this gap by enhancing the effectiveness of RL policies:

 * Robust RL Optimization: We delve into the strategic use of perturbations to 
   refine policies learned from simulators. This approach focuses on increasing
   the adaptability and robustness of these policies, making them better suited
   for real-world applications where variability and unexpected conditions are
   common.
 * Offline RL Optimization: Further discussion will explore the application of
   the Hamilton-Jacobi-Bellman (HJB) equation as a method to enhance the
   performance of policies trained on static datasets. This technique is crucial
   for improving real-world applicability in scenarios where real-time
   interaction with the environment is not possible.