Slotted ALOHA Based on Reinforcement Learning with Thompson Sampling

2021 ◽  
Vol 46 (10) ◽  
pp. 1646-1649
Author(s):  
Yeong-Je Jo ◽  
Gyung-Ho Hwang
Keyword(s):  
Reinforcement Learning ◽  
Slotted Aloha ◽  
Thompson Sampling

scholarly journals On Thompson Sampling and Asymptotic Optimality

2017 ◽  
Author(s):  
Jan Leike ◽  
Tor Lattimore ◽  
Laurent Orseau ◽  
Marcus Hutter
Keyword(s):  
Reinforcement Learning ◽  
Asymptotic Optimality ◽  
Thompson Sampling ◽  
Stochastic Environments ◽  
Optimal Value ◽  
Partially Observable ◽  
General Stochastic

We discuss some recent results on Thompson sampling for nonparametric reinforcement learning in countable classes of general stochastic environments. These environments can be non-Markovian, non-ergodic, and partially observable. We show that Thompson sampling learns the environment class in the sense that (1) asymptotically its value converges in mean to the optimal value and (2) given a recoverability assumption regret is sublinear. We conclude with a discussion about optimality in reinforcement learning.

Deep-IRSA: A Deep Reinforcement Learning Approach to Irregular Repetition Slotted ALOHA

2021 ◽  
Author(s):  
Ibrahim Ayoub ◽  
Iman Hmedoush ◽  
Cedric Adjih ◽  
Kinda Khawam ◽  
Samer Lahoud
Keyword(s):  
Reinforcement Learning ◽  
Learning Approach ◽  
Slotted Aloha

scholarly journals Making slotted ALOHA efficient and fair using reinforcement learning

2021 ◽  
Author(s):  
Molly Zhang ◽  
Luca de Alfaro ◽  
J.J. Garcia-Luna-Aceves
Keyword(s):  
Reinforcement Learning ◽  
Slotted Aloha

Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reinforcement Learning ◽  
Iowa Gambling Task ◽  
Bayes Factors ◽  
Gambling Task ◽  
Learning Models ◽  
Reinforcement Learning Models
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