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EVOW
2003
Springer
14 years 2 months ago
Exploring the T-Maze: Evolving Learning-Like Robot Behaviors Using CTRNNs
Abstract. This paper explores the capabilities of continuous time recurrent neural networks (CTRNNs) to display reinforcement learning-like abilities on a set of T-Maze and double ...
Jesper Blynel, Dario Floreano
ICANN
1997
Springer
14 years 1 months ago
Minimalistic Approach to 3D Obstacle Avoidance Behavior from Simulated Evolution
We present a minimalistic approach to establish obstacle avoidance and course stabilization behavior of a simulated flying autonomous agent in a 3D virtual world. The agent uses v...
Titus R. Neumann, Susanne A. Huber, Heinrich H. B&...
IJCNN
2006
IEEE
14 years 3 months ago
Global Reinforcement Learning in Neural Networks with Stochastic Synapses
— We have found a more general formulation of the REINFORCE learning principle which had been proposed by R. J. Williams for the case of artificial neural networks with stochast...
Xiaolong Ma, Konstantin Likharev
ESANN
2004
13 years 10 months ago
Neural networks for data mining: constrains and open problems
When we talk about using neural networks for data mining we have in mind the original data mining scope and challenge. How did neural networks meet this challenge? Can we run neura...
Razvan Andonie, Boris Kovalerchuk
IWANN
2009
Springer
14 years 3 months ago
Switching Dynamics of Neural Systems in the Presence of Multiplicative Colored Noise
We study the dynamics of a simple bistable system driven by multiplicative correlated noise. Such system mimics the dynamics of classical attractor neural networks with an addition...
Jorge F. Mejías, Joaquín J. Torres, ...