Results 21 to 30 of about 6,944,236 (307)
RL-GRIT: Reinforcement Learning for Grammar Inference [PDF]
2021 IEEE Security and Privacy Workshops (SPW), 2021 When working to understand usage of a data format, examples of the data format are often more representative than the format's specification. For example, two different applications might use very different JSON representations, or two PDF-writing applications might make use of very different areas of the PDF specification to realize the same rendered ...
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Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle
IEEE Access, 2020 Autonomous underwater vehicle (AUV) plays an increasingly important role in ocean exploration. Existing AUVs are usually not fully autonomous and generally limited to pre-planning or pre-programming tasks.
Qilei Zhang +4 more
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CST-RL: Contrastive Spatio-Temporal Representations for Reinforcement Learning
IEEE Access, 2023 Learning representations from high-dimensional observations is critical for training of pixel-based continuous control tasks with reinforcement learning (RL).
Chi-Kai Ho, Chung-Ta King
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Learning the Quadruped Robot by Reinforcement Learning (RL)
Iraqi Journal for Electrical and Electronic Engineering, 2022 In this paper, a simulation was utilized to create and test the suggested controller and to investigate the ability of a quadruped robot based on the SimScape-Multibody toolbox, with PID controllers and deep deterministic policy gradient DDPG Reinforcement learning (RL) techniques.
A. Issa, A. Aldair
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A review of reinforcement learning based hyper-heuristics [PDF]
PeerJ Computer ScienceThe reinforcement learning based hyper-heuristics (RL-HH) is a popular trend in the field of optimization. RL-HH combines the global search ability of hyper-heuristics (HH) with the learning ability of reinforcement learning (RL). This synergy allows the
Cuixia Li +4 more
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RL$^3$: Boosting Meta Reinforcement Learning via RL inside RL$^2$
CoRR, 2023 Meta reinforcement learning (Meta-RL) methods such as RL$^2$ have emerged as promising approaches for learning data-efficient RL algorithms tailored to a given task distribution. However, they show poor asymptotic performance and struggle with out-of-distribution tasks because they rely on sequence models, such as recurrent neural networks or ...
Abhinav Bhatia +2 more
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IEEE Access, 2018 In recent years, reinforcement learning (RL) has achieved remarkable success due to the growing adoption of deep learning techniques and the rapid growth of computing power.
Tuyen P. Le +2 more
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Safe and Efficient Operation with Constrained Hierarchical Reinforcement Learning
, 2023 Hierarchical Reinforcement Learning (HRL) holds the promise of enhancing sample efficiency and generalization capabilities of Reinforcement Learning (RL) agents by leveraging task decomposition and temporal abstraction, which aligns with human reasoning.
Günnemann, Stephan +2 more
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Co-Evolution of Predator-Prey Ecosystems by Reinforcement Learning Agents
Entropy, 2021 The problem of finding adequate population models in ecology is important for understanding essential aspects of their dynamic nature. Since analyzing and accurately predicting the intelligent adaptation of multiple species is difficult due to their ...
Jeongho Park +4 more
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A new model of decision processing in instrumental learning tasks
eLife, 2021 Learning and decision-making are interactive processes, yet cognitive modeling of error-driven learning and decision-making have largely evolved separately.
Steven Miletić +5 more
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