scholarly journals ReLeaSER: A Reinforcement Learning Strategy for Optimizing Utilization Of Ephemeral Cloud Resources

2020 ◽  
Author(s):  
Mohamed Handaoui ◽  
Jean-Emile Dartois ◽  
Jalil Boukhobza ◽  
Olivier Barais ◽  
Laurent d'Orazio
Keyword(s):  
Reinforcement Learning ◽  
Learning Strategy ◽  
Cloud Resources

A New Hybrid Deep Neural Architectural Search based Ensemble Reinforcement Learning Strategy for Wind Power Forecasting

2021 ◽  
pp. 1-1
Author(s):  
Seyed Mohammad Jafar Jalali ◽  
Gerardo J. Osorio ◽  
Sajad Ahmadian ◽  
Mohamed Lotfi ◽  
Vasco Campos ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Wind Power ◽  
Learning Strategy ◽  
Wind Power Forecasting ◽  
Power Forecasting

scholarly journals Severe Sexual Abuse Reduces Frontoparietal Network Activity during Model-Based Reinforcement Learning Updates

2019 ◽  
Author(s):  
Allison Letkiewicz ◽  
Amy L. Cochran ◽  
Josh M. Cisler
Keyword(s):  
Sexual Abuse ◽  
Reinforcement Learning ◽  
Learning Styles ◽  
Computational Models ◽  
Learning Strategy ◽  
Network Activity ◽  
Model Based ◽  
Model Free ◽  
Frontoparietal Network ◽  
Assaultive Trauma

Trauma and trauma-related disorders are characterized by altered learning styles. Two learning processes that have been delineated using computational modeling are model-free and model-based reinforcement learning (RL), characterized by trial and error and goal-driven, rule-based learning, respectively. Prior research suggests that model-free RL is disrupted among individuals with a history of assaultive trauma and may contribute to altered fear responding. Currently, it is unclear whether model-based RL, which involves building abstract and nuanced representations of stimulus-outcome relationships to prospectively predict action-related outcomes, is also impaired among individuals who have experienced trauma. The present study sought to test the hypothesis of impaired model-based RL among adolescent females exposed to assaultive trauma. Participants (n=60) completed a three-arm bandit RL task during fMRI acquisition. Two computational models compared the degree to which each participant’s task behavior fit the use of a model-free versus model-based RL strategy. Overall, a greater portion of participants’ behavior was better captured by the model-based than model-free RL model. Although assaultive trauma did not predict learning strategy use, greater sexual abuse severity predicted less use of model-based compared to model-free RL. Additionally, severe sexual abuse predicted less left frontoparietal network encoding of model-based RL updates, which was not accounted for by PTSD. Given the significant impact that sexual trauma has on mental health and other aspects of functioning, it is plausible that altered model-based RL is an important route through which clinical impairment emerges.

Multi-UAV-enabled AoI-aware WPCN: A Multi-agent Reinforcement Learning Strategy

2021 ◽  
Author(s):  
Omar Sami Oubbati ◽  
Mohammed Atiquzzaman ◽  
Abderrahmane Lakas ◽  
Abdullah Baz ◽  
Hosam Alhakami ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Strategy ◽  
Multi Agent ◽  
Multi Uav

scholarly journals Reinforcement learning-based link adaptation in long delayed underwater acoustic channel

2019 ◽  
Vol 283 ◽  
pp. 07001 ◽  
Author(s):  
Jingxi Wang ◽  
Chau Yuen ◽  
Yong Liang Guan ◽  
Fengxiang Ge
Keyword(s):  
Reinforcement Learning ◽  
Learning Strategy ◽  
Learning Algorithm ◽  
Optimal Parameters ◽  
Underwater Acoustic Channel ◽  
Transfer Data ◽  
Data Packets ◽  
Significant Area ◽  
Channel Environment ◽  
Self Learning

In this paper, we apply reinforcement learning, a significant area of machine learning, to formulate an optimal self-learning strategy to interact in an unknown and dynamically variable underwater channel. The dynamic and volatile nature of the underwater channel environment makes it impossible to employ pre-knowledge. In order to select the optimal parameters to transfer data packets, by using reinforcement learning, this problem could be resolved, and better throughput could be achieved without any environmental pre-information. The slow sound velocity in an underwater scenario, means that the delay of transmitting packet acknowledgement back to sender from the receiver is material, deteriorating the convergence speed of the reinforcement learning algorithm. As reinforcement learning requires a timely acknowledgement feedback from the receiver, in this paper, we combine a juggling-like ARQ (Automatic Repeat Request) mechanism with reinforcement learning to minimize the long-delayed reward feedback problem. The simulation is accomplished by OPNET.

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