d4rl

Introduces CODS, an iterative critic-guided data selection method for offline reinforcement learning that retains task performance at low data budgets by selecting high-residual transitions over multiple rounds.

This paper introduces a neuro-inspired framework called Inverter that uses Inverse Learning (IL) for fast and efficient planning and control, achieving significant improvements on D4RL benchmarks and quantum gate synthesis with orders of magnitude less inference computation.