General Flexible $f$-divergence for Challenging Offline RL Datasets with Low Stochasticity and Diverse Behavior Policies

General Flexible $f$-divergence for Challenging Offline RL Datasets with Low Stochasticity and Diverse Behavior Policies explores Improve offline RL performance using flexible $f$-divergence constraints for better dataset adaptation.. Commercial viability score: 5/10 in Reinforcement Learning.