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» Learning Precise Timing with LSTM Recurrent Networks
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131
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BC
2002
193views more  BC 2002»
14 years 11 months ago
Resonant spatiotemporal learning in large random recurrent networks
Taking a global analogy with the structure of perceptual biological systems, we present a system composed of two layers of real-valued sigmoidal neurons. The primary layer receives...
Emmanuel Daucé, Mathias Quoy, Bernard Doyon
65
Voted
ICANN
2007
Springer
15 years 5 months ago
Solving Deep Memory POMDPs with Recurrent Policy Gradients
Abstract. This paper presents Recurrent Policy Gradients, a modelfree reinforcement learning (RL) method creating limited-memory stochastic policies for partially observable Markov...
Daan Wierstra, Alexander Förster, Jan Peters,...
87
Voted
ICES
2003
Springer
125views Hardware» more  ICES 2003»
15 years 4 months ago
Evolving Reinforcement Learning-Like Abilities for Robots
Abstract. In [8] Yamauchi and Beer explored the abilities of continuous time recurrent neural networks (CTRNNs) to display reinforcementlearning like abilities. The investigated ta...
Jesper Blynel
103
Voted
EVOW
2003
Springer
15 years 4 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
CORR
2008
Springer
69views Education» more  CORR 2008»
14 years 11 months ago
Solving Time of Least Square Systems in Sigma-Pi Unit Networks
The solving of least square systems is a useful operation in neurocomputational modeling of learning, pattern matching, and pattern recognition. In these last two cases, the soluti...
Pierre Courrieu