International Journal of Robotics Research
Linear least-squares algorithms for temporal difference learning
Machine Learning - Special issue on reinforcement learning
Natural gradient works efficiently in learning
Neural Computation
Neural control of rhythmic arm movements
Neural Networks - Special issue on neural control and robotics: biology and technology
Walknet—a biologically inspired network to control six-legged walking
Neural Networks - Special issue on neural control and robotics: biology and technology
Reinforcement learning based on on-line EM algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
Control of exploitation-exploration meta-parameter in reinforcement learning
Neural Networks - Computational models of neuromodulation
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Least-Squares Methods in Reinforcement Learning for Control
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
SIAM Journal on Control and Optimization
Automatic basis function construction for approximate dynamic programming and reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
ECML'05 Proceedings of the 16th European conference on Machine Learning
Gradient calculations for dynamic recurrent neural networks: a survey
IEEE Transactions on Neural Networks
Learning to Move in Modular Robots using Central Pattern Generators and Online Optimization
International Journal of Robotics Research
Flexible Control Mechanism for Multi-DOF Robotic Arm Based on Biological Fluctuation
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
The Neuromodulatory System: A Framework for Survival and Adaptive Behavior in a Challenging World
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Direct programming of a central pattern generator for periodic motions by touching
Robotics and Autonomous Systems
A self-organizing map for controlling artificial locomotion
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Neural oscillators programming simplified
Applied Computational Intelligence and Soft Computing
Chaotic exploration and learning of locomotion behaviors
Neural Computation
Fuzzy SVM learning control system considering time properties of biped walking samples
Engineering Applications of Artificial Intelligence
DCOB: Action space for reinforcement learning of high DoF robots
Autonomous Robots
Fast damage recovery in robotics with the T-resilience algorithm
International Journal of Robotics Research
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Animals' rhythmic movements, such as locomotion, are considered to be controlled by neural circuits called central pattern generators (CPGs), which generate oscillatory signals. Motivated by this biological mechanism, studies have been conducted on the rhythmic movements controlled by CPG. As an autonomous learning framework for a CPG controller, we propose in this article a reinforcement learning method we call the ''CPG-actor-critic'' method. This method introduces a new architecture to the actor, and its training is roughly based on a stochastic policy gradient algorithm presented recently. We apply this method to an automatic acquisition problem of control for a biped robot. Computer simulations show that training of the CPG can be successfully performed by our method, thus allowing the biped robot to not only walk stably but also adapt to environmental changes.