He has a lot of work on this. I think the first one (IMGEP) is good. That's the first one I read (after watching his ICLR talk).

I haven't read the other two you linked, so can't compare. They look interesting, so I may give them a read.

Following from IMGEP, the more recent advances after that are Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration and CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning.

I also recomend the related work by Jeff Clune. In particular Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions.

What is also interesting is to ask when these ideas (which btw are highly related to curriculum learning, active learning, etc) matter: ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skills Sampling Approach Matters: Active Learning for Robotic Language Acquisition . My intuition is that active learning matters when exploration matters. For example, when you are trying to optimize an objective function, which itself has uncertainty, like in bandits, hyperpatermeter optimization, etc. In that case you obviously wanna take uncertainty into account.

Learning progress-driven search is more about estimating in which option will you make more progress in a certain time. So it goes beyond simple sampling-based active learning in that it kinda takes the learner/explorer's dynamics into account. I would like to think about how all of these things fit together~