Multi-Task and Goal-Conditioned RL, Sharing Weights and Sharing Data | Stanford CS224R Lecture 12
Multi-task RL treats which task you are on as part of the state, s = (s̄, z_i), so the problem is still an ordinary MDP and standard RL algorithms still apply. Lecture 12 of CS224R covers two kinds of sharing: weight sharing, where one network conditioned on z_i does every task, and data sharing via hindsight relabeling, where data collected for task A gets relabeled as data for task B. Goal-conditioned RL is the special case where the task is a goal state; relabeling with the state you actually reached eases the exploration problem of sparse rewards. Data sharing has three prerequisites: consistent dynamics across tasks, a reward you can evaluate, and an off-policy algorithm.