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David Mark Bossens

2 accepted papers

2025

Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration

ICLR 2025poster

Imitation learning is a central problem in reinforcement learning where the goal is to learn a policy that mimics the expert's behavior. In practice, it is often challenging to learn the expert policy from a limited number of demonstrations accurately due to the complexity of the state space. Moreov…

Cited by 0SourcePDFScholar
2025

Diversifying Robot Locomotion Behaviors with Extrinsic Behavioral Curiosity

ICML 2025poster

Imitation learning (IL) has shown promise in robot locomotion but is often limited to learning a single expert policy, constraining behavior diversity and robustness in unpredictable real-world scenarios. To address this, we introduce Quality Diversity Inverse Reinforcement Learning (QD-IRL), a nove…