Bayesian reinforcement learning for single-episode missions in partially unknown environments
We consider planning for mobile robots conducting missions in realworld domains where a priori unknown dynamics affect the robot’s costs and transitions. We study single-episode missions where it is crucial that the robot appropriately trades off exploration and exploitation, such that the learning...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
OpenReview
2022
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