Results 21 to 30 of about 198,528 (317)
International Journal of Cognitive Computing in Engineering, 2021 Bi-objective school bus scheduling optimization problem that is a subset of vehicle fleet scheduling problem is focused in this paper. In the literature, school bus routing and scheduling problem is proven to be an NP-Hard problem.
Eda Koksal +3 more
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Comparing policy gradient and value function based reinforcement learning methods in simulated electrical power trade [PDF]
, 2012 In electrical power engineering, reinforcement learning algorithms can be used to model the strategies of electricity market participants. However, traditional value function based reinforcement learning algorithms suffer from convergence issues when ...
Burt, Graeme +7 more
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A neural network model for the orbitofrontal cortex and task space acquisition during reinforcement learning. [PDF]
PLoS Computational Biology, 2018 Reinforcement learning has been widely used in explaining animal behavior. In reinforcement learning, the agent learns the value of the states in the task, collectively constituting the task state space, and uses the knowledge to choose actions and ...
Zhewei Zhang +4 more
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Real-World Reinforcement Learning
, 2021 This is our first public release of real-world reinforcement learning, a document with associated code showing how to design and deploy reinforcement learning solutions in customer-facing applications.If you use this software, please cite it as ...
Mason, Douglas
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Quantum Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008 13 pages, 7 figures ...
Daoyi Dong +3 more
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Reactive Reinforcement Learning in Asynchronous Environments
Frontiers in Robotics and AI, 2018 The relationship between a reinforcement learning (RL) agent and an asynchronous environment is often ignored. Frequently used models of the interaction between an agent and its environment, such as Markov Decision Processes (MDP) or Semi-Markov Decision
Jaden B. Travnik +6 more
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Photonic reinforcement learning based on optoelectronic reservoir computing
Scientific Reports, 2022 Reinforcement learning has been intensively investigated and developed in artificial intelligence in the absence of training data, such as autonomous driving vehicles, robot control, internet advertising, and elastic optical networks.
Kazutaka Kanno, Atsushi Uchida
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On the convergence of reinforcement learning [PDF]
Journal of Economic Theory, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +4 more sources
Curriculum Learning in Reinforcement Learning [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Transfer learning in reinforcement learning is an area of research that seeks to speed up or improve learning of a complex target task, by leveraging knowledge from one or more source tasks. This thesis will extend the concept of transfer learning to curriculum learning, where the goal is to design a sequence of source tasks for an agent to train on ...
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Learning to Optimize for Reinforcement Learning
CoRR, 2023 Published at RLC 2024.
Qingfeng Lan +3 more
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