Feature-based methods for large scale dynamic programming
Machine Learning - Special issue on reinforcement learning
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiple model-based reinforcement learning
Neural Computation
Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Reinforcement learning with via-point representation
Neural Networks
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
Learning motor primitives for robotics
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Algorithms
Universal approximation bounds for superpositions of a sigmoidal function
IEEE Transactions on Information Theory
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Reinforcement learning (RL) for robot control is an important technology for future robots since it enables us to design a robot's behavior using the reward function. However, RL for high degree-of-freedom robot control is still an open issue. This paper proposes a discrete action space DCOB which is generated from the basis functions (BFs) given to approximate a value function. The remarkable feature is that, by reducing the number of BFs to enable the robot to learn quickly the value function, the size of DCOB is also reduced, which improves the learning speed. In addition, a method WF-DCOB is proposed to enhance the performance, where wire-fitting is utilized to search for continuous actions around each discrete action of DCOB. We apply the proposed methods to motion learning tasks of a simulated humanoid robot and a real spider robot. The experimental results demonstrate outstanding performance.