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Full-Text Articles in Engineering
On Step Sizes, Stochastic Shortest Paths, And Survival Probabilities In Reinforcement Learning, Abhijit Gosavi
On Step Sizes, Stochastic Shortest Paths, And Survival Probabilities In Reinforcement Learning, Abhijit Gosavi
Engineering Management and Systems Engineering Faculty Research & Creative Works
Reinforcement learning (RL) is a simulation-based technique useful in solving Markov decision processes if their transition probabilities are not easily obtainable or if the problems have a very large number of states. We present an empirical study of (i) the effect of step-sizes (learning rules) in the convergence of RL algorithms, (ii) stochastic shortest paths in solving average reward problems via RL, and (iii) the notion of survival probabilities (downside risk) in RL. We also study the impact of step sizes when function approximation is combined with RL. Our experiments yield some interesting insights that will be useful in practice …
Forecasting Series-Based Stock Price Data Using Direct Reinforcement Learning, H. Li, Cihan H. Dagli, David Lee Enke
Forecasting Series-Based Stock Price Data Using Direct Reinforcement Learning, H. Li, Cihan H. Dagli, David Lee Enke
Engineering Management and Systems Engineering Faculty Research & Creative Works
A significant amount of work has been done in the area of price series forecasting using soft computing techniques, most of which are based upon supervised learning. Unfortunately, there has been evidence that such models suffer from fundamental drawbacks. Given that the short-term performance of the financial forecasting architecture can be immediately measured, it is possible to integrate reinforcement learning into such applications. In this paper, we present the novel hybrid view for a financial series and critic adaptation stock price forecasting architecture using direct reinforcement. A new utility function called policies-matching ratio is also proposed. The need for the …
Combining Evolving Neural Network Classifiers Using Bagging, Sunghwan Sohn, Cihan H. Dagli
Combining Evolving Neural Network Classifiers Using Bagging, Sunghwan Sohn, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
The performance of the neural network classifier significantly depends on its architecture and generalization. It is usual to find the proper architecture by trial and error. This is time consuming and may not always find the optimal network. For this reason, we apply genetic algorithms to the automatic generation of neural networks. Many researchers have provided that combining multiple classifiers improves generalization. One of the most effective combining methods is bagging. In bagging, training sets are selected by resampling from the original training set and classifiers trained with these sets are combined by voting. We implement the bagging technique into …
An Enhanced Least-Squares Approach For Reinforcement Learning, Hailin Li, Cihan H. Dagli
An Enhanced Least-Squares Approach For Reinforcement Learning, Hailin Li, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
This paper presents an enhanced least-squares approach for solving reinforcement learning control problems. Model-free least-squares policy iteration (LSPI) method has been successfully used for this learning domain. Although LSPI is a promising algorithm that uses linear approximator architecture to achieve policy optimization in the spirit of Q-learning, it faces challenging issues in terms of the selection of basis functions and training samples. Inspired by orthogonal least-squares regression (OLSR) method for selecting the centers of RBF neural network, we propose a new hybrid learning method. The suggested approach combines LSPI algorithm with OLSR strategy and uses simulation as a tool to …
Using A Neuro-Fuzzy-Genetic Data Mining Architecture To Determine A Marketing Strategy In A Charitable Organization's Donor Database, Korakot Hemsathapat, Cihan H. Dagli, David Lee Enke
Using A Neuro-Fuzzy-Genetic Data Mining Architecture To Determine A Marketing Strategy In A Charitable Organization's Donor Database, Korakot Hemsathapat, Cihan H. Dagli, David Lee Enke
Engineering Management and Systems Engineering Faculty Research & Creative Works
This paper describes the use of a neuro-fuzzy-genetic data mining architecture for finding hidden knowledge and modeling the data of the 1997 donation campaign of an American charitable organization. This data was used during the 1998 KDD Cup competition. In the architecture, all input variables are first preprocessed and all continuous variables are fuzzified. Principal component analysis (PCA) is then applied to reduce the dimensions of the input variables in finding combinations of variables, or factors, that describe major trends in the data. The reduced dimensions of the input variables are then used to train probabilistic neural networks (PNN) to …
Derivation Of Fuzzy Membership Functions Using One-Dimensional Self-Organizing Maps, Thomas E. Sandidge, Cihan H. Dagli
Derivation Of Fuzzy Membership Functions Using One-Dimensional Self-Organizing Maps, Thomas E. Sandidge, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
This paper discusses a system of self-organizing maps that approximate the fuzzy membership function for an arbitrary number of fuzzy classes. This is done through the ordering and clustering properties of one-dimensional self-organizing maps and iterative approximation of conditional probabilities of nodes in one map being the winner given that a node in the other map is the winner. Application of this system reduces fuzzy membership design time to that required to train the system of self-organizing maps.