# OGBench

Offline reinforcement learning usually trains its critic by regressing onto a bootstrapped target, stabilized with a target network. AL-LBLP keeps the Lifted Bellman Linear Programming form, which writes in-sample Bellman optimality as a linear program over the dataset, and solves it with the method of multipliers: the fixed hinge coefficients become multipliers learned by dual ascent, leaving one penalty parameter. With a single critic, no target network and no ensemble, a Gaussian actor, a flow actor and best-of-n sampling average 55.8, 55.7 and 60.1 on the 50 state-based OGBench tasks.

Offline reinforcement learning usually trains its critic by regressing onto a bootstrapped target, stabilized with a target network. AL-LBLP keeps the Lifted Bellman Linear Programming form, which writes in-sample Bellman optimality as a linear program over the dataset, and solves it with the method of multipliers: the fixed hinge coefficients become multipliers learned by dual ascent, leaving one penalty parameter. With a single critic, no target network and no ensemble, a Gaussian actor, a flow actor and best-of-n sampling average 55.8, 55.7 and 60.1 on the 50 state-based OGBench tasks.