ERLARS 2014 - 7th International Workshop on Evolutionary and Reinforcement Learning for Autonomous Robot Systems

Evolutionary and Reinforcement Learning methods are important learning approaches for neural networks and other knowledge representations. They are inspired by nature and known to be used extensively in biological systems. However, so far their use in artificial cognitive systems, e.g. autonomous robot systems, is limited. This is mainly due to the large number of necessary robot actions and/or learning cycles before an acceptable mapping from perceptions to actions is found. Autonomous robots are becoming more and more common even in non-industrial settings. However, they tend to have very limited learning capabilities. These are usually restricted to adjusting a few parameters in an otherwise fixed control strategy that determines how the robot interacts with the environment.
Website: www.erlars.org/2014/