Generating neural architectures from parameter spaces for multi-agent reinforcement learning - Artaud, Corentin; De-Silva, Varuna; Pina, Rafael; Shi, Xiyu (Pattern recognition letters, 2024)
We explore a data-driven approach to generating neural network parameters to determine whether generative models can capture the underlying distribution of a collection of neural network checkpoints. We compile a dataset of checkpoints from neural networks trained within the multi-agent reinforcement learning framework, thus potentially producing previously unseen combinations of neural network parameters. In particular, our generative model is a conditional transformer-based variational autoencoder that, when provided with random noise and a specified performance metric – in our context, returns – predicts the appropriate distribution over the parameter space to achieve the desired performance metric. Our method successfully generates parameters for a specified optimal return without further fine-tuning. We also show that the parameters generated using this approach are more constrained and less variable and, most importantly, perform on par with those trained directly under the multi-agent reinforcement learning framework. We test our method on the neural network architectures commonly employed in the most advanced state-of-the-art algorithms. •Variational autoencoders with multi-head self-attention architecture.•Generate dataset of neural networks and augmentation processes.•Generating from random noise neural network parameter conditioned on return.•Discuss implications in the context of MARL and perform analysis of generations vs traditional.