Learning Reward Machines from Partially Observed Optimal Policies
Published in arXiv, 2025
This paper deals with learning reward machines (FSM) from partial expert policies.
Published in arXiv, 2025
This paper deals with learning reward machines (FSM) from partial expert policies.
Published in L4DC, 2024
This paper is about the Identifiability of Max Entropy Inverse Reinforcement Learning.
Recommended citation: Shehab, M. L., Aspeel, A., Aréchiga, N., Best, A., & Ozay, N. (2024, June). Learning true objectives: Linear algebraic characterizations of identifiability in inverse reinforcement learning. In 6th Annual Learning for Dynamics & Control Conference (pp. 1266-1277). PMLR.
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Published in ITSC, 2022
This paper is about perfomring anomally detection using LP Inverse Reinforcement Learning.
Recommended citation: Li, D., Shehab, M. L., Liu, Z., Aréchiga, N., DeCastro, J., & Ozay, N. (2022, October). Outlier-robust inverse reinforcement learning and reward-based detection of anomalous driving behaviors. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (pp. 4175-4182). IEEE.
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