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Online No-regret Model-Based Meta RL for Personalized Navigation

arXiv 2204.01925 ↗First posted 5 Apr 2022cs.LGcited by 0

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Abstract (arXiv)

The interaction between a vehicle navigation system and the driver of the vehicle can be formulated as a model-based reinforcement learning problem, where the navigation systems (agent) must quickly adapt to the characteristics of the driver (environmental dynamics) to provide the best sequence of turn-by-turn driving instructions. Most modern day navigation systems (e.g, Google maps, Waze, Garmin) are not designed to personalize their low-level interactions for individual users across a wide range of driving styles (e.g., vehicle type, reaction time, level of expertise). Towards the development of personalized navigation systems that adapt to a variety of driving styles, we propose an online no-regret model-based RL method that quickly conforms to the dynamics of the current user. As the user interacts with it, the navigation system quickly builds a user-specific model, from which navigation commands are optimized using model predictive control. By personalizing the policy in this way, our method is able to give well-timed driving instructions that match the user's dynamics. Our theoretical analysis shows that our method is a no-regret algorithm and we provide the convergence rate in the agnostic setting. Our empirical analysis with 60+ hours of real-world user data using a driving simulator shows that our method can reduce the number of collisions by more than 60%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 44 references that are in the map. First posted on arXiv 5 Apr 2022.

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25 of 44 references.

References · 25

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13 more
  1. 2021Learning to drive from a world on railscited by 114
  2. 2018Model-Based Reinforcement Learning via Meta-Policy Optimizationcited by 101
  3. 2020Semantic Audio-Visual Navigationcited by 82
  4. 2018Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RLcited by 81
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  7. 2018The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpointcited by 40
  8. 2020Model-based Adversarial Meta-Reinforcement Learningcited by 33
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  13. 2016Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translationcited by 12

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