arXiv cs.LG
· Papers
CODS: Iterative Bellman-Residual Data Selection for Reusable Offline Reinforcement Learning
arXiv:2608.07719v1 Announce Type: new Abstract: Offline reinforcement learning repeatedly trains policies from a fixed transition pool, making redundant data costly across seeds and hyperparameters, while naive subsampling can remove rare transitions needed for long-horizon credit assignment. We introduce CODS, a criti