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Going Beyond State-Reaching: Learning Abstractions for Intrinsically Motivated Option Discovery
Temporal abstraction via options can improve exploration in large environments. However, existing option discovery algorithms find subgoals that target all aspects of the state simultaneously. This state-reaching approach produces options that only apply in narrow regions of the state-space, eventua
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