Technical Note: \cal Q-Learning
Machine Learning
Gradient descent for general reinforcement learning
Proceedings of the 1998 conference on Advances in neural information processing systems II
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolving Neural Control Systems
IEEE Expert: Intelligent Systems and Their Applications
Evolving neural networks through augmenting topologies
Evolutionary Computation
Symbiotic Evolution of Neural Networks in Sequential Decision Tasks
Symbiotic Evolution of Neural Networks in Sequential Decision Tasks
Robust non-linear control through neuroevolution
Robust non-linear control through neuroevolution
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Learning finite-state controllers for partially observable environments
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A common genetic encoding for both direct and indirect encodings of networks
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Restricted gradient-descent algorithm for value-function approximation in reinforcement learning
Artificial Intelligence
Modular neuroevolution for multilegged locomotion
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Accelerating neuroevolutionary methods using a Kalman filter
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolving neural networks for fractured domains
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
The Journal of Machine Learning Research
Analysis of an evolutionary reinforcement learning method in a multiagent domain
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
The 2007 IEEE CEC simulated car racing competition
Genetic Programming and Evolvable Machines
Evolving Efficient Connection for the Design of Artificial Neural Networks
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Fitness Expectation Maximization
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Evolution Strategies for Direct Policy Search
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Evolving Neural Networks for Online Reinforcement Learning
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Variable Metric Reinforcement Learning Methods Applied to the Noisy Mountain Car Problem
Recent Advances in Reinforcement Learning
Stochastic search using the natural gradient
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Neuroevolutionary reinforcement learning for generalized helicopter control
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
NEAT in increasingly non-linear control situations
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Neuroevolution strategies for episodic reinforcement learning
Journal of Algorithms
Machine learning for event selection in high energy physics
Engineering Applications of Artificial Intelligence
Knowledge-based recurrent neural networks in Reinforcement Learning
ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
Speeding up reinforcement learning using recurrent neural networks in non-Markovian environments
ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
Neuro-evolution approaches to collective behavior
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
2006: celebrating 75 years of AI - history and outlook: the next 25 years
50 years of artificial intelligence
Machine learning techniques for selforganizing combustion control
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Autonomous Agents and Multi-Agent Systems
Evolving neural networks in compressed weight space
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A NEAT Way for Evolving Echo State Networks
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Theoretical convergence guarantees for cooperative coevolutionary algorithms
Evolutionary Computation
Proceedings of the 8th International Conference on Frontiers of Information Technology
On the deleterious effects of a priori objectives on evolution and representation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Sequential constant size compressors for reinforcement learning
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Adaptive reservoir computing through evolution and learning
Neurocomputing
Learning parameters of linear models in compressed parameter space
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
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Many complex control problems are not amenable to traditional controller design. Not only is it difficult to model real systems, but often it is unclear what kind of behavior is required. Reinforcement learning (RL) has made progress through direct interaction with the task environment, but it has been difficult to scale it up to large and partially observable state spaces. In recent years, neuroevolution, the artificial evolution of neural networks, has shown promise in tasks with these two properties. This paper introduces a novel neuroevolution method called CoSyNE that evolves networks at the level of weights. In the most extensive comparison of RL methods to date, it was tested in difficult versions of the pole-balancing problem that involve large state spaces and hidden state. CoSyNE was found to be significantly more efficient and powerful than the other methods on these tasks, forming a promising foundation for solving challenging real-world control tasks.