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Found 3 papers, 2 papers with code
Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference
Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles.
A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning
Model-based Reinforcement Learning (MBRL) aims to make agents more sample-efficient, adaptive, and explainable by learning an explicit model of the environment.
A Bayesian Approach to Robust Inverse Reinforcement Learning
We consider a Bayesian approach to offline model-based inverse reinforcement learning (IRL).
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