scholarly journals Reinforcement Learning with Raw Image Pixels as Input State

2006 ◽  
pp. 446-454 ◽  
Author(s):  
Damien Ernst ◽  
Raphaël Marée ◽  
Louis Wehenkel
Keyword(s):  
Reinforcement Learning ◽  
Input State ◽  
Image Pixels

A Neural Network Based Automatic Generation Controller Design through Reinforcement Learning

2006 ◽  
Vol 6 (1) ◽  
Author(s):  
Imthias Ahamed T.P. ◽  
Nagendra Rao P.S. ◽  
Sastry P.S.
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Learning Automata ◽  
Automatic Generation ◽  
Training Data ◽  
Input State ◽  
Continuous Variables ◽  
Rbf Neural Networks ◽  
State Variables ◽  
Q Learning

This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm, for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.

scholarly journals Joint Entity and Relation Extraction with a Hybrid Transformer and Reinforcement Learning Based Model

2020 ◽  
Vol 34 (05) ◽  
pp. 9314-9321
Author(s):  
Ya Xiao ◽  
Chengxiang Tan ◽  
Zhijie Fan ◽  
Qian Xu ◽  
Wenye Zhu
Keyword(s):  
Reinforcement Learning ◽  
Noisy Data ◽  
Relation Extraction ◽  
Input State ◽  
Data Filtering ◽  
The Public ◽  
Distant Supervision ◽  
Proposed Model ◽  
Public Dataset ◽  
Relation Classification

Joint extraction of entities and relations is a task that extracts the entity mentions and semantic relations between entities from the unstructured texts with one single model. Existing entity and relation extraction datasets usually rely on distant supervision methods which cannot identify the corresponding relations between a relation and the sentence, thus suffers from noisy labeling problem. We propose a hybrid deep neural network model to jointly extract the entities and relations, and the model is also capable of filtering noisy data. The hybrid model contains a transformer-based encoding layer, an LSTM entity detection module and a reinforcement learning-based relation classification module. The output of the transformer encoder and the entity embedding generated from the entity detection module are combined as the input state of the reinforcement learning module to improve the relation classification and noisy data filtering. We conduct experiments on the public dataset produced by the distant supervision method to verify the effectiveness of our proposed model. Different experimental results show that our model gains better performance on entity and relation extraction than the compared methods and also has the ability to filter noisy sentences.

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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