Results 31 to 40 of about 122,392 (268)
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
Quantum Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008 13 pages, 7 figures ...
Daoyi Dong +3 more
openaire +4 more sources
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
doaj +1 more source
On the convergence of reinforcement learning [PDF]
Journal of Economic Theory, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +5 more sources
On the Use of Deep Reinforcement Learning for Visual Tracking: A Survey
IEEE Access, 2021 This paper aims at highlighting cutting-edge research results in the field of visual tracking by deep reinforcement learning. Deep reinforcement learning (DRL) is an emerging area combining recent progress in deep and reinforcement learning.
Giorgio Cruciata +2 more
doaj +1 more source
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 ...
openaire +3 more sources
Learning to Optimize for Reinforcement Learning
CoRR, 2023 Published at RLC 2024.
Qingfeng Lan +3 more
openaire +3 more sources
Reinforcement Learning to Rank [PDF]
Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019 Interactive systems such as search engines or recommender systems are increasingly moving away from single-turn exchanges with users. Instead, series of exchanges between the user and the system are becoming mainstream, especially when users have complex needs or when the system struggles to understand the user's intent.
openaire +3 more sources