Invited Talk: Increasing Representational Power and Scaling Inference in Reinforcement Learning

2012 ◽  
pp. 2-2
Author(s):  
Kristian Kersting
Keyword(s):  
Reinforcement Learning ◽  
Representational Power

scholarly journals Accelerating Deep Neuroevolution on Distributed FPGAs for Reinforcement Learning Problems

2021 ◽  
Vol 17 (2) ◽  
pp. 1-17
Author(s):  
Alexis Asseman ◽  
Nicolas Antoine ◽  
Ahmet S. Ozcan
Keyword(s):  
Reinforcement Learning ◽  
High Performance ◽  
Mesh Network ◽  
System Level ◽  
Evolutionary Strategies ◽  
Robotic Control ◽  
Training Time ◽  
The Neural Network ◽  
Representational Power ◽  
Level Parallelism

Reinforcement learning, augmented by the representational power of deep neural networks, has shown promising results on high-dimensional problems, such as game playing and robotic control. However, the sequential nature of these problems poses a fundamental challenge for computational efficiency. Recently, alternative approaches such as evolutionary strategies and deep neuroevolution demonstrated competitive results with faster training time on distributed CPU cores. Here we report record training times (running at about 1 million frames per second) for Atari 2600 games using deep neuroevolution implemented on distributed FPGAs. Combined hardware implementation of the game console, image preprocessing and the neural network in an optimized pipeline, multiplied with the system level parallelism enabled the acceleration. These results are the first application demonstration on the IBM Neural Computer, which is a custom designed system that consists of 432 Xilinx FPGAs interconnected in a 3D mesh network topology. In addition to high performance, experiments also showed improvement in accuracy for all games compared to the CPU implementation of the same algorithm.

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

Research on Signal Control Method of Single Intersection Based on Reinforcement Learning

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Yilong Ren ◽  
Le Zhang ◽  
Han Jiang ◽  
Chengsheng Liu
Keyword(s):  
Reinforcement Learning ◽  
Control Method ◽  
Signal Control
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