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The development of safe autonomous vehicles (AVs) poses a challenging task as it requires algorithms that can make real-time decisions in unpredictable circumstances. Reinforcement learning (RL) presents a promising approach for AV control, as it utilizes trial and error to enable optimal decision-making.
Aug 31, 2023 · We present our approach for the development, validation and deployment of a data-driven decision-making function for the automated control of a vehicle.
Aug 31, 2023 · The training is conducted by means of proximal policy optimisation (PPO), a state of the art algorithm from the field of reinforcement learning.
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Dec 15, 2024 · Reinforcement learning (RL) can improve autonomous vehicles (AVs) by supporting adaptive decision-making, but it faces challenges in ...
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Focusing on autonomous driving safety and interactivity, this thesis presents novel contributions on topics including safe and robust reinforcement learning, ...
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Feb 16, 2024 · Integrating Reinforcement Learning (RL) in safety-critical applications, such as autonomous vehicles, healthcare, and industrial automation, ...
It is the first time to apply the proposed learning-based framework for safety-guaranteed tracking control of automated vehicles with uncertainties.
This paper introduces an innovative reinforcement learning-based controller that incorporates the control barrier function (CBF) approaches for an optimal ...
Jun 6, 2024 · In this paper, we propose Goal-conditioned Scenario Generation (GOOSE), a goal-conditioned reinforcement learning (RL) approach that automatically generates ...
Jun 28, 2023 · Controllers based on reinforcement learning (RL) are particularly promising for autonomous driving, being able to optimize a combination of ...