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Found 3 papers, 1 papers with code
GRIMGEP: Learning Progress for Robust Goal Sampling in Visual Deep Reinforcement Learning
However, a currently known limitation of agents trying to maximize the diversity of sampled goals is that they tend to get attracted to noise or more generally to parts of the environments that cannot be controlled (distractors).
Autonomous Goal Exploration using Learned Goal Spaces for Visuomotor Skill Acquisition in Robots
The automatic and efficient discovery of skills, without supervision, for long-living autonomous agents, remains a challenge of Artificial Intelligence.
Curiosity Driven Exploration of Learned Disentangled Goal Spaces
Finally, we show that the measure of learning progress, used to drive curiosity-driven exploration, can be used simultaneously to discover abstract independently controllable features of the environment.
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