Policy Improvement

Policy improvement in reinforcement learning focuses on reliably enhancing existing policies, particularly in high-stakes scenarios where safety is paramount. Current research emphasizes developing algorithms that guarantee policy improvement with high probability, often employing techniques like multi-step learning, dual regularization, and ensemble methods to address challenges such as distributional shifts and uncertainty in value estimations. These advancements are crucial for deploying reinforcement learning in real-world applications, such as autonomous driving and healthcare, where safe and efficient learning is essential. The field is also exploring methods to improve sample efficiency and robustness to various forms of bias, including backdoors and adversarial attacks.