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• W3147335029 abstract "Driving in a dynamic, multi-agent, and complex urban environment is a difficult task requiring a complex decision-making policy. The learning of such a policy requires a state representation that can encode the entire environment. Mid-level representations that encode a vehicle’s environment as images have become a popular choice. Still, they are quite high-dimensional, limiting their use in data-hungry approaches such as reinforcement learning. In this article, we propose to learn a low-dimensional and rich latent representation of the environment by leveraging the knowledge of relevant semantic factors. To do this, we train an encoder-decoder deep neural network to predict multiple application-relevant factors such as the trajectories of other agents and the ego car. Furthermore, we propose a hazard signal based on other vehicles’ future trajectories and the planned route which is used in conjunction with the learned latent representation as input to a down-stream policy. We demonstrate that using the multi-head encoder-decoder neural network results in a more informative representation than a standard single-head model. In particular, the proposed representation learning and the hazard signal help reinforcement learning to learn faster, with increased performance and less data than baseline methods." @default.
• W3147335029 created "2021-04-13" @default.
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• W3147335029 date "2022-09-01" @default.
• W3147335029 modified "2023-10-14" @default.
• W3147335029 title "Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learning" @default.
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• W3147335029 doi "https://doi.org/10.1109/tiv.2022.3149891" @default.
• W3147335029 hasPublicationYear "2022" @default.
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