In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

Dealing with high-dimensional input spaces, like visual input, is a challenging task for
reinforcement learning (RL). Neuroevolution (NE), used for continuous RL problems, has to …

This paper addresses the general problem of reinforcement learning (RL) in partially
observable environments. In 2013, our large RL recurrent neural networks (RNNs) learned …

In the absence of external guidance, how can a robot learn to map the many raw pixels of
high-dimensional visual inputs to useful action sequences? We propose here Continual …

We introduce here an incremental version of slow feature analysis (IncSFA), combining
candid covariance-free incremental principal components analysis (CCIPCA) and …

Feature representation is critical not only for pattern recognition tasks but also for
reinforcement learning (RL) methods to solve learning control problems under uncertainties …

Orseau and Ring, as well as Dewey, have recently described problems, including self-
delusion, with the behavior of agents using various definitions of utility functions. An agent's …

Dealing with high-dimensional input spaces, like visual input, is a challenging task for
reinforcement learning (RL). Neuroevolution (NE), used for continuous RL problems, has to …

Neuroevolution, the artificial evolution of neural networks, has shown great promise on
continuous reinforcement learning tasks that require memory. However, it is not yet directly …

Die katholische Kirche gilt vordergründig mit ihren aufwändigen Liturgien oder einem
romantisch anmutenden Klosterleben als ideale Medienreligion. Mit eigenen …