Preference-Augmented Distributional Reinforcement Learning for Risk-Sensitive Navigation
Overview
This research introduces a distributional reinforcement learning approach that models risk preference variations as shifts in quantile-weight distributions. By exposing agents to diverse preferences during training via preference-guided exploration and optimizing against worst-case distributions at deployment, the method aims to create conservative policies robust to multiple risk profiles without heavy reliance on external sensor signals or manual hyperparameter tuning. Experiments indicate reduced collisions and improved task success across simulated and real-world vehicle platforms.
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- arXiv Robotics — research abstractsSignalPreference-Augmented Distributional Reinforcement Learning for Risk-Sensitive Navigation
This research introduces a distributional reinforcement learning approach that models risk preference variations as shifts in quantile-weight distributions. By exposing agents to diverse preferences during training via preference-guided exploration and optimizing against worst-case distributions at deployment, the method aims to create conservative policies robust to multiple risk profiles without heavy reliance on external sensor signals or manual hyperparameter tuning. Experiments indicate reduced collisions and improved task success across simulated and real-world vehicle platforms.
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