  Optimising deep neural networks is a challenging task due to complex training
dynamics, high computational requirements, and long training times. To address
this difficulty, we propose the framework of Generalisable Agents for Neural
Network Optimisation (GANNO) -- a multi-agent reinforcement learning (MARL)
approach that learns to improve neural network optimisation by dynamically and
responsively scheduling hyperparameters during training. GANNO utilises an
agent per layer that observes localised network dynamics and accordingly takes
actions to adjust these dynamics at a layerwise level to collectively improve
global performance. In this paper, we use GANNO to control the layerwise
learning rate and show that the framework can yield useful and responsive
schedules that are competitive with handcrafted heuristics. Furthermore, GANNO
is shown to perform robustly across a wide variety of unseen initial
conditions, and can successfully generalise to harder problems than it was
trained on. Our work presents an overview of the opportunities that this
paradigm offers for training neural networks, along with key challenges that
remain to be overcome.
