Construction of PSE System for Developing Reinforcement Learning Algorithms
نویسندگان
چکیده
Many researchers who study stochastic simulations such as reinforcement learning algorithms have to run their programs many times to compare developing algorithms and find better sets of parameters for their programs. In order to reduce their working time, we build a problem solving environments (PSE) system to assist them. Our system has three sub-systems: a distributed computing system, a data management system and a graph generation system. In this paper, we present a relationship between developing algorithms and the three sub-systems. Using our system, users register their programs, run them on a distributed computing system, obtain results automatically, and compare them graphically. We conduct experiments with human subjects. As a result, a user obtained five times speedup for his work time through executions, screening simulation data sets and comparing algorithms. As these subsystems for PSE system, it is important not only distributed computing but also supporting both data management and graph generation.
منابع مشابه
Developing a similarity searching module for patient safety event reporting system using semantic similarity measures
BACKGROUND The most important knowledge in the field of patient safety is regarding the prevention and reduction of patient safety events (PSE) during treatment and care. The similarities and patterns among the events may otherwise go unnoticed if they are not properly reported and analyzed. There is an urgent need for developing a PSE reporting system that can dynamically measure the similarit...
متن کاملReinforcement Learning in Neural Networks: A Survey
In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...
متن کاملReinforcement Learning in Neural Networks: A Survey
In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...
متن کاملLow-Area/Low-Power CMOS Op-Amps Design Based on Total Optimality Index Using Reinforcement Learning Approach
This paper presents the application of reinforcement learning in automatic analog IC design. In this work, the Multi-Objective approach by Learning Automata is evaluated for accommodating required functionalities and performance specifications considering optimal minimizing of MOSFETs area and power consumption for two famous CMOS op-amps. The results show the ability of the proposed method to ...
متن کاملA Multiagent Reinforcement Learning algorithm to solve the Community Detection Problem
Community detection is a challenging optimization problem that consists of searching for communities that belong to a network under the assumption that the nodes of the same community share properties that enable the detection of new characteristics or functional relationships in the network. Although there are many algorithms developed for community detection, most of them are unsuitable when ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- JCIT
دوره 5 شماره
صفحات -
تاریخ انتشار 2010