Brains, Behavior and Robotics
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Acquisition of Stand-up Behavior by a Real Robot using Hierarchical Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
The Neuromodulatory System: A Framework for Survival and Adaptive Behavior in a Challenging World
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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In this paper, we propose a dynamic allocation method of basis functions, an Allocation/Elimination Gaussian Softmax Basis Function Network (AE-GSBFN), that is used in reinforcement learning AE-GSBFN is a kind of actor-critic method that uses basis functions This method can treat continuous high-dimensional state spaces, because basis functions required only for learning are dynamically allocated, and if an allocated basis function is identified as redundant, the function is eliminated This method overcomes the curse of dimensionality and avoids a fall into local minima through the allocation and elimination processes To confirm the effectiveness of our method, we used a maze task to compare our method with an existing method, which has only an allocation process Moreover, as learning of continuous high-dimensional state spaces, our method was applied to motion control of a humanoid robot We demonstrate that the AE-GSBFN is capable of providing better performance than the existing method.