  What goals should a multi-goal reinforcement learning agent pursue during
training in long-horizon tasks? When the desired (test time) goal distribution
is too distant to offer a useful learning signal, we argue that the agent
should not pursue unobtainable goals. Instead, it should set its own intrinsic
goals that maximize the entropy of the historical achieved goal distribution.
We propose to optimize this objective by having the agent pursue past achieved
goals in sparsely explored areas of the goal space, which focuses exploration
on the frontier of the achievable goal set. We show that our strategy achieves
an order of magnitude better sample efficiency than the prior state of the art
on long-horizon multi-goal tasks including maze navigation and block stacking.
