Offline reinforcement learning (RL) has garnered significant interest due to its safe and easily scalable paradigm. which essentially requires training policies from pre-collected datasets without the need for additional environment interaction. However. training under this paradigm presents its own challenge: the extrapolation error stemming from out-of-distribution (OOD) data. https://hrzckxekqnni5o.theisblog.com/35125351/reconstructing-nonparametric-productivity-networks