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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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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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Experience-weighted Attraction Learning in Normal Form Games [PDF]
, 1999 In ‘experience-weighted attraction’ (EWA) learning, strategies have attractions that reflect initial predispositions, are updated based on payoff experience, and determine choice probabilities according to some rule (e.g., logit).
Camerer, Colin F., Ho, Teck Hua
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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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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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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
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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 +3 more
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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
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Eligibility Propagation to Speed up Time Hopping for Reinforcement Learning [PDF]
, 2009 A mechanism called Eligibility Propagation is proposed to speed up the Time Hopping technique used for faster Reinforcement Learning in simulations. Eligibility Propagation provides for Time Hopping similar abilities to what eligibility traces provide ...
Dong, Fangyan +3 more
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