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Reinforcement Learning

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Reinforcement Learning

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Research Frontiers in Inverse Reinforcement Learning Theory

Research on inferring reward functions and objectives from observed expert behavior to enable imitation and understanding of agent goals.

Reward Ambiguity and Human Preference Extraction
Multi-Agent Inverse Reinforcement Learning at Scale
Non-Stationary Reward Functions in Dynamic Environments
Interpretability Through Inverse Reinforcement Learning
Reward Learning From Implicit Human Feedback
Inverse RL in Sparse and Delayed Reward Settings
Decoding Latent Objectives From Suboptimal Demonstrations
Inverse Reinforcement Learning for Preference Alignment
Uncertainty Quantification in Inferred Reward Models
Hierarchical Reward Structure Recovery From Behavior

All Reinforcement Learning PhD categories