Conservative Q Learning

Conservative Q-learning (CQL) is an offline reinforcement learning algorithm designed to mitigate the risk of overestimating value functions, a common problem when learning from static datasets. Current research focuses on improving CQL's performance and robustness through techniques like incorporating novel neural network architectures (e.g., Kolmogorov-Arnold Networks), addressing data imbalances, and developing more nuanced approaches to pessimism in value estimation. These advancements are significant because they enhance the reliability and applicability of offline RL in various domains, including robotics, healthcare, and resource management, where online learning is impractical or unsafe.