Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 8 of 8 for “"Safe Reinforcement Learning"”.
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Nonlinear adaptation of established linear and predictive control laws through safe reinforcement learning
Reinforcement learning (RL) enables the prospect of data-driven controllers that learn to select control actions optimally purely through the feedback provided by an evaluative signal (the reward). In principle, this technology may be used to develop adaptive controllers that account for …
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SAFE REINFORCEMENT LEARNING-BASED GREEN LIGHT OPTIMAL SPEED ADVISORY FOR MIXED-TRAFFIC PLATOONS
This thesis develops a platoon-centric, safe RL-based Green Light Optimal Speed Advisory (GLOSA) system to optimize the CAV speed profile of a mixed-traffic platoon. First, we design a multi-agent RL algorithm to achieve a balance between the energy and travel efficiency of a mixed-traffic platoon, …
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Safe reinforcement learning: An overview, a hybrid systems perspective, and a case study
Reinforcement learning (RL) is a general method for agents to learn optimal control policies through exploration and experience. Due to its generality, RL can generate novel policies that may not be easily expressed with rules-based strategies or traditional control techniques. Over the years since …
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Safe and adaptive reinforcement learning for robotics applications
In recent years, learning-based control methods, especially those leveraging the power of reinforcement learning (RL) and deep learning, have demonstrated impressive performance in complex robotics control tasks. However, they often suffer from the lack of safety and robustness guarantees, which …
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Enhancing Safety in Reinforcement Learning Training through Transformers Filtering
L'abstract è presente nell'allegato / the abstract is in the attachment
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Deep Learning and Reinforcement Learning for Industrial Microgrid Energy Management
L'abstract è presente nell'allegato / the abstract is in the attachment
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Reinforcement Learning Control for Mobile Robot Parking with Safety Constraints
… thesis studies a hybrid framework that combines reinforcement learning (RL) with control barrier function (CBF)-based methods to achieve safe autonomous vehicle control, focusing on parking with obstacle avoidance. We apply Deep Deterministic Policy Gradient (DDPG) methods for continuous control …
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Real-world autonomy in uncertain and unknown environments
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms