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Derrik E. Asher

2 accepted papers

2026

Direct Preference Optimization for Primitive-Enabled Hierarchical RL: A Bilevel Approach

ICLR 2026poster

Hierarchical reinforcement learning (HRL) enables agents to solve complex, long-horizon tasks by decomposing them into manageable sub-tasks. However, HRL methods face two fundamental challenges: (i) non-stationarity caused by the evolving lower-level policy during training, which destabilizes higher…

Cited by 0SourceScholar
2025

QuasiNav: Asymmetric Cost-Aware Navigation Planning with Constrained Quasimetric Reinforcement Learning

ICRA 2025

Autonomous navigation in unstructured outdoor environments is inherently challenging due to the presence of asymmetric traversal costs, such as varying energy expenditures for uphill versus downhill movement. Traditional reinforcement learning methods often assume symmetric costs, which can lead to

Cited by 0SourceScholar