Results 31 to 40 of about 5,876,040 (331)
On the convergence of reinforcement learning [PDF]
Journal of Economic Theory, 2005 Abstract This paper examines the convergence of payoffs and strategies in Erev and Roth's model of reinforcement learning. When all players use this rule it eliminates iteratively dominated strategies and in two-person constant-sum games average payoffs converge to the value of the game.
openaire +5 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
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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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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
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A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation
Italian National Conference on Sensors, 2023 Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems.
Dong-Ki Han +3 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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The Dreaming Variational Autoencoder for Reinforcement Learning Environments [PDF]
, 2018 Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent ...
K Arulkumaran +4 more
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A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Science, 2018 One program to rule them all Computers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular game, exploiting its properties, such as the symmetries of the board on which
David Silver +12 more
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Applied Sciences, 2023 Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists.
S. Sahu, A. Mokhade, N. Bokde
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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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