Thread by @shaneguML

If overwhelmed by # of papers in *offline* RL, check out our @NeurIPSConf Spotlight with Scott Fujimoto: we show how few lines change to TD3 (TD3+BC) can be competitive with SoTA algorithms, halving training time. Inspired by #minimalism #zen #konmari arxiv.org/abs/2106.06860

We propose "BC as a regularizer", which adds negligible compute cost to original TD3 objective, but makes it quite performative on offline RL.

For the table, we followed similar "algorithm" "implementation" separations suggested in our other NeurIPS paper

https://twitter.com/shaneguML/status/1466960547640147971?s=20

Lastly, we appreciate constructive feedback from reviewers and suggesting this "one git-pull"-size paper for Spotlight at @NeurIPSConf! The community could benefit from simple ideas.