Gradient descent for general reinforcement learning
Proceedings of the 1998 conference on Advances in neural information processing systems II
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Sequential cost-sensitive decision making with reinforcement learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Applications of the self-organising map to reinforcement learning
Neural Networks - New developments in self-organizing maps
Empirical Comparison of Various Reinforcement Learning Strategies for Sequential Targeted Marketing
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Cross channel optimized marketing by reinforcement learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Intelligent negotiation behaviour model for an open railway access market
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
In this work, RL is used to find an optimal policy for a marketing campaign. Data show a complex characterization of state and action spaces. Two approaches are proposed to circumvent this problem. The first approach is based on the self-organizing map (SOM), which is used to aggregate states. The second approach uses a multilayer perceptron (MLP) to carry out a regression of the action-value function. The results indicate that both approaches can improve a targeted marketing campaign. Moreover, the SOM approach allows an intuitive interpretation of the results, and the MLP approach yields robust results with generalization capabilities.