  Offline reinforcement learning (RL) aims to learn an optimal policy from
pre-collected and labeled datasets, which eliminates the time-consuming data
collection in online RL. However, offline RL still bears a large burden of
specifying/handcrafting extrinsic rewards for each transition in the offline
data. As a remedy for the labor-intensive labeling, we propose to endow offline
RL tasks with a few expert data and utilize the limited expert data to drive
intrinsic rewards, thus eliminating the need for extrinsic rewards. To achieve
that, we introduce \textbf{C}alibrated \textbf{L}atent
g\textbf{U}idanc\textbf{E} (CLUE), which utilizes a conditional variational
auto-encoder to learn a latent space such that intrinsic rewards can be
directly qualified over the latent space. CLUE's key idea is to align the
intrinsic rewards consistent with the expert intention via enforcing the
embeddings of expert data to a calibrated contextual representation. We
instantiate the expert-driven intrinsic rewards in sparse-reward offline RL
tasks, offline imitation learning (IL) tasks, and unsupervised offline RL
tasks. Empirically, we find that CLUE can effectively improve the sparse-reward
offline RL performance, outperform the state-of-the-art offline IL baselines,
and discover diverse skills from static reward-free offline data.
