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cs.AI2026
Going Beyond State-Reaching: Learning Abstractions for Intrinsically Motivated Option Discovery
Akhil Bagaria, Anita De Mello Koch, George Konidaris
Temporal abstraction via options can improve exploration in large environments. However, existing option discovery algorithms find subgoals that target all aspects of the state sim…
cs.AI2025
Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning
Martin Klissarov, Akhil Bagaria, Ziyan Luo +3
Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intelligence (AI). Hierarchical reinforcement le…