ABSTRACT

In recent years machine learning research has been dominated by optimisation-based learning methods (take gradient descent, for example, which is ubiquitous in deep learning). However, while tools that operate under this paradigm have proven to be very powerful, they are often not well suited for tackling complex challenges such as highly non-stationary targets or explicit multi-agent systems. In an attempt to overcome such limitations, some researchers are instead turning towards open-ended methods, and considering how to design the underlying learning dynamics.

This tutorial discusses how different tools can be applied to construct and combine adaptive objectives for populations of learners. We begin by providing background on the problem setting, basic tools and philosophy. In a second part we then dive into the basics of evolutionary computation. In particular, we frame the development of evolutionary methods as a focus shift away from gradient-free optimisers in search of more generic and powerful tools for designing learning dynamics. Finally, we provide a more detailed overview of techniques and research around training and evaluating populations of agents.

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