Online Reinforcement Learning

Online reinforcement learning (RL) focuses on training agents to make optimal decisions in dynamic environments through continuous interaction and feedback. Current research emphasizes improving sample efficiency, particularly through offline data pre-training and techniques like prioritized experience replay and ensemble methods, as well as exploring novel model architectures such as Kolmogorov-Arnold Networks. These advancements aim to address challenges like reward sparsity, distribution shifts between offline and online data, and the need for safe and reliable learning in high-stakes applications such as robotics and healthcare, ultimately leading to more robust and efficient RL agents.