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\newcommand{\trtitle}{Outline - Second Environmental Feedback Loop}
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		Universit\"at Hamburg\\
		Department Informatik\\
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\section{Introduction}
\label{sec:introduction}

Autonomous robots need to adapt to new situations. They have a need to learn
for an entire life. In order to do this they need a second environmental feedback
loop that tells them when to learn.\cite{Toutounji2016}

The learning poses another problem as well. The previously learned weights
are usually largely forgotten, which is known as catastrophic forgetting.\cite{French1999}

Since catastrophic forgetting is a key problem for autonomous learning, it is
crucial to overcome it. In this paper I will present some approaches for
learning in an autonomous setup to analyse which of them if any can overcome
catastrophic forgetting. Attempts to overcome that were made by
Kirkpatrick\cite{Kirkpatrick2017}, Velez\cite{Velez2017} and Shmelkov\cite{Shmelkov2017}.

\section{Neuromodulation}
\label{sec:neuromodulation}

Neuromodulation is a way to implement the second environmental feedback loop.
A Modulated Neural Network (MNN) contains neuromodulator cells (NMC), which are
attached to carrier neurons with a modulatory subnetwork (MSN).

Neuromodulation can also be done based upon Diffusion.

\section{Plasticity}
\label{sec:plasticity}

Plasticity can be realized by various approaches. Here three approaches are
presented, Modulated Random Search, Modulated Gaussian Walk and Diffusion based
neuromodulation.

\subsection{Modulated Random Search}
\label{subsec:mrs}

Does things.

\subsection{Modulated Gaussian Walk}
\label{subsec:mgw}

Does things more efficient.

\subsection{Localized learning}
\label{subsec:diffusion}

Velez describe another approach that employs
modularity for the learning. Essentially this results in task-specific localized
learning and functional modules for each subtask.

\section{Comparison regarding catastrophic forgetting}
\label{sec:comparison}

Modulated Random Search is not at all useful for overcoming catastrophic forgetting.
Modulated Gaussian Walk improves to that end.
Localized learning overcomes catastrophic forgetting for small
networks.

\section{Conclusion}
\label{sec:concl}

A second environmental feedback loop is important to tell autonomous systems
when to learn. But the method to learn is important as well to be any use
in a practical environment. A comparison has shown that localized learning
can overcome catastrophic forgetting for small networks.

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