Results 21 to 30 of about 9,252,589 (289)
Reinforcement Learning-based Spectrum Sharing for Cognitive Radio [PDF]
, 2011 This thesis investigates how distributed reinforcement learning-based resource assignment algorithms can be used to improve the performance of a cognitive radio system.
Jiang, Tao
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Complex & Intelligent Systems, 2023 Robot navigation in crowded environments has recently benefited from advances in deep reinforcement learning (DRL) approaches. However, it still presents a challenge to designing socially compliant robot behavior.
Estrella Elvia Montero +3 more
doaj +1 more source
Deep Reinforcement Learning methods for StarCraft II Learning Environment [PDF]
, 2022 Reinforcement Learning (RL) is a Machine Learning framework in which an agent learns to solve a task by trial-and-error interaction with the surrounding environment.
Dainese, Nicola
core
Formal Verification of Spatio-Temporal Rules Guided Safe Reinforcement Learning for CPS [PDF]
Jisuanji kexue yu tansuoDeep reinforcement learning is currently a commonly used method in decision-making for cyber physical system (CPS). However, when facing an unknown environment and dealing with complex tasks, deep reinforcement learning based on black boxes cannot ...
YIN Chan, ZHU Yi, WANG Jinyong, CHEN Xiaoying, HAO Guosheng
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Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving
CoRR, 2022 We present a novel hybrid learning method, HyLEAR, for solving the collision-free navigation problem for self-driving cars in POMDPs. HyLEAR leverages interposed learning to embed knowledge of a hybrid planner into a deep reinforcement learner to faster determine safe and comfortable driving policies.
Dikshant Gupta, Matthias Klusch
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IEEE Access, 2023 In this paper, we present an advanced adaptive cruise control (ACC) concept powered by Deep Reinforcement Learning (DRL) that generates safe, human-like, and comfortable car-following policies. Unlike the current trend in developing DRL-based ACC systems,
M. Ugur Yavas +2 more
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, 2023 We summarize the IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL2023)1, held at the 32nd International Joint Conference on Artificial Intelligence (IJCAI-23) on August 21-22, 2023 in Macau, China.
Pedroza, Gabriel +13 more
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Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments
2022 International Conference on Robotics and Automation (ICRA), 2022 Deep reinforcement learning (RL) has shown promising results in the motion planning of manipulators. However, no method guarantees the safety of highly dynamic obstacles, such as humans, in RL-based manipulator control. This lack of formal safety assurances prevents the application of RL for manipulators in real-world human environments.
Thumm, Jakob, Althoff, Matthias
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Deep Reinforcement Learning Approaches for the Game of Briscola [PDF]
, 2023 openReinforcement learning is increasingly becoming one of the most interesting areas of research in recent years. It is a machine learning approach that aims to design autonomous agents capable of learning from interaction with the envi- ronment ...
SINGH, AMANPREET
core
Inductive biases and generalisation for deep reinforcement learning [PDF]
, 2021 In this thesis we aim to improve generalisation in deep reinforcement learning. Generalisation is a fundamental challenge for any type of learning, determining how acquired knowledge can be transferred to new, previously unseen situations.
Igl, Maximilian
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