SEAR: Sample Efficient Action Chunking Reinforcement Learning

C F Maximilian Nagy, Onur Celik, Emiliyan Gospodinov, Florian Seligmann, Weiran Liao, Aryan Kaushik, Gerhard Neumann

Repository layout

Directory	Contents
`SEAR/`	SEAR for online RL on Metaworld (plus the SimbaV2 / naive-chunking baselines)
`sear_qc/`	SEAR-QC: SEAR's multi-horizon targets and random replanning on top of Q-Chunking (OGBench)
`CQN-AS/`	CQN-AS baseline
`docs/`	Project website, served with GitHub Pages

See the README in each directory for installation and reproduction commands.