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Stimulus sampling as an exploration mechanism for fast reinforcement learning

Publications associées (113)

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Human perceptual learning by mental imagery

Michael Herzog, Fred Mast, Elisa Tartaglia, Laura Bamert

Perceptual learning is learning to perceive. For example, a radiologist is able to easily identify anomalies in medical images only after extended training. Theoretical and psychophysical studies [1-12] suggest that such improvements of performance are acc ...

Elsevier2009

Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward- based Scenarios

Dario Floreano, Claudio Mattiussi, Peter Dürr, Andrea Soltoggio

memory in biological neural networks. Similarly, artificial neural networks could benefit from modulatory dynamics when facing certain types of learning problem. Here we test this hypothesis by introducing modulatory neurons to enhance or dampen neural pla ...

MIT Press2008

Neuroevolution: from architectures to learning

Dario Floreano, Claudio Mattiussi, Peter Dürr

Artificial neural networks are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and ...

2008

Evolving Neuromodulatory Topologies for Reinforcement Learning-like Problems

Dario Floreano, Claudio Mattiussi, Peter Dürr, Andrea Soltoggio

Environments with varying reward contingencies constitute a challenge to many living creatures. In such conditions, animals capable of adaptation and learning derive an advantage. Recent studies suggest that neuromodulatory dynamics are a key factor in reg ...

IEEE Press2007

Slowness: An Objective for Spike-Timing-Dependent Plasticity?

Slow Feature Analysis (SFA) is an efficient algorithm for learning input-output functions that extract the most slowly varying features from a quickly varying signal. It has been successfully applied to the unsupervised learning of translation-, rotation-, ...

Public Library of Science2007

Adding prediction risk to the theory of reward learning

This article analyzes the simple Rescorla-Wagner learning rule from the vantage point of least squares learning theory. In particular, it suggests how measures of risk, such as prediction risk, can be used to adjust the learning constant in reinforcement l ...

2007

Adding prediction risk to the theory of reward learning

2007

Effects of stress and genotype on meta-parameter dynamics in reinforcement learning

Maria del Carmen Sandi Perez, Wulfram Gerstner, Gediminas Luksys, Jérémie Knüsel

Stress and genetic background regulate different aspects of behavioral learning through the action of stress hormones and neuromodulators. In reinforcement learning (RL) models, meta-parameters such as learning rate, future reward discount factor, and expl ...

2007

In search of the neural circuits of intrinsic motivation

Frédéric Kaplan

Children seem to acquire new know-how in a continuous and open-ended manner. In this paper, we hypothesize that an intrinsic motivation to progress in learning is at the origins of the remarkable structure of children’s developmental trajectories. In this ...

2007

Spiking Neuron Networks A survey

Spiking Neuron Networks (SNNs) are often referred to as the 3rd generation of neural networks. They derive their strength and interest from an accurate modelling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs ...

IDIAP2006