When multiple tasks need improvements, fine-tuning a generalist policy becomes tricky. How do we allocate a demonstration budget across a set of tasks of varied difficulty and familiarity? We are presenting a possible solution at ICML on Wednesday! (1/3)

Last week I presented our last work: 🐝“Epistemically-guided forward backward exploration (FBEE)”🐝 at the RL_Conference TLDR: Active learning for unsupervised RL