Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Generality and Difficulty in Genetic Programming: Evolving a Sort
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
Least-squares policy iteration
The Journal of Machine Learning Research
Evolution of a human-competitive quantum fourier transform algorithm using genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Tree-Based Batch Mode Reinforcement Learning
The Journal of Machine Learning Research
Evolutionary Design of Arbitrarily Large Sorting Networks Using Development
Genetic Programming and Evolvable Machines
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Hierarchical genetic algorithms operating on populations of computer programs
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
From program verification to program synthesis
Proceedings of the 37th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Generating three binary addition algorithms using reinforcement programming
Proceedings of the 48th Annual Southeast Regional Conference
Kernel-Based Least Squares Policy Iteration for Reinforcement Learning
IEEE Transactions on Neural Networks
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Reinforcement Programming (RP) is a new approach to automatically generating algorithms that uses reinforcement learning techniques. This paper introduces the RP approach and demonstrates its use to generate a generalized, in-place, iterative sort algorithm. The RP approach improves on earlier results that use genetic programming (GP). The resulting algorithm is a novel algorithm that is more efficient than comparable sorting routines. RP learns the sort in fewer iterations than GP and with fewer resources. Experiments establish interesting empirical bounds on learning the sort algorithm: A list of size 4 is sufficient to learn the generalized sort algorithm. The training set only requires one element and learning took less than 200,000 iterations. Additionally RP was used to generate three binary addition algorithms: a full adder, a binary incrementer, and a binary adder. © 2012 Wiley Periodicals, Inc.