Results 21 to 30 of about 640,433 (264)
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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Reinforcement learning in populations of spiking neurons [PDF]
, 2008 Population coding is widely regarded as a key mechanism for achieving reliable behavioral responses in the face of neuronal variability. But in standard reinforcement learning a flip-side becomes apparent.
A Pouget +13 more
core +2 more sources
Inverse Reinforcement Learning without Reinforcement Learning
, 2023 Inverse Reinforcement Learning (IRL) is a powerful set of techniques for imitation learning that aims to learn a reward function that rationalizes expert demonstrations. Unfortunately, traditional IRL methods suffer from a computational weakness: they require repeatedly solving a hard reinforcement learning (RL) problem as a subroutine. This is counter-
Swamy, Gokul +3 more
openaire +2 more sources
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
Journal of the American Statistical Association, 2015 In this article, we introduce a new type of tree-based method, reinforcement learning trees (RLT), which exhibits significantly improved performance over traditional methods such as random forests (Breiman 2001) under high-dimensional settings. The innovations are three-fold.
Ruoqing, Zhu +2 more
openaire +3 more sources
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
Towards Continual Reinforcement Learning through Evolutionary Meta-Learning [PDF]
, 2019 In continual learning, an agent is exposed to a changing environment, requiring it to adapt during execution time. While traditional reinforcement learning (RL) methods have shown impressive results in various domains, there has been less progress in ...
Grbic, Djordje, Risi, Sebastian
core +1 more source
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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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
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Frontiers in Psychology, 2021 Reinforcement learning relies on the reward prediction error (RPE) signals conveyed by the midbrain dopamine system. Previous studies showed that dopamine plays an important role in both positive and negative reinforcement.
Shuyuan Xu +6 more
doaj +1 more source