Proceedings of the third international conference on Genetic algorithms
Genetic Reinforcement Learning for Neurocontrol Problems
Machine Learning - Special issue on genetic algorithms
Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Feature-based methods for large scale dynamic programming
Machine Learning - Special issue on reinforcement learning
Symbiotic evolution of neural networks in sequential decision tasks
Symbiotic evolution of neural networks in sequential decision tasks
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Learning to Drive a Bicycle Using Reinforcement Learning and Shaping
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Least-squares policy iteration
The Journal of Machine Learning Research
Robust non-linear control through neuroevolution
Robust non-linear control through neuroevolution
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
Restricted gradient-descent algorithm for value-function approximation in reinforcement learning
Artificial Intelligence
Design and Analysis of Learning Classifier Systems: A Probabilistic Approach (Studies in Computational Intelligence)
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
The Journal of Machine Learning Research
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This work provides a systematic review of the criteria most commonly used to classify sequential decision problems and discusses their impact on the performance of reinforcement learning and evolutionary computation. The paper also proposes a further division of one class of decision problems into two subcategories, which delimits a set of decision tasks particularly difficult for optimization techniques in general and evolutionary methods in particular. A simple computational experiment is presented to illustrate the subject.