  Learning models of artificial intelligence can nowadays perform very well on
a large variety of tasks. However, in practice different task environments are
best handled by different learning models, rather than a single, universal,
approach. Most non-trivial models thus require the adjustment of several to
many learning parameters, which is often done on a case-by-case basis by an
external party. Meta-learning refers to the ability of an agent to autonomously
and dynamically adjust its own learning parameters, or meta-parameters. In this
work we show how projective simulation, a recently developed model of
artificial intelligence, can naturally be extended to account for meta-learning
in reinforcement learning settings. The projective simulation approach is based
on a random walk process over a network of clips. The suggested meta-learning
scheme builds upon the same design and employs clip networks to monitor the
agent's performance and to adjust its meta-parameters "on the fly". We
distinguish between "reflexive adaptation" and "adaptation through learning",
and show the utility of both approaches. In addition, a trade-off between
flexibility and learning-time is addressed. The extended model is examined on
three different kinds of reinforcement learning tasks, in which the agent has
different optimal values of the meta-parameters, and is shown to perform well,
reaching near-optimal to optimal success rates in all of them, without ever
needing to manually adjust any meta-parameter.
