Generalized Weight Agnostic Neural Networks for Configurable and Continual Autonomous Systems

Pranav U. Gadikar, Vinod Ganesan, Pratyush Kumar Panda

Abstract: Implementing pervasive intelligence faces the challenges of efficient, continual, and configurable learning on embedded devices. We address these challenges with two novel extensions to Weight Agnostic Neural Networks (WANNs) namely – (i) Multi-weight extension and (ii) Multi-objective extension. In the multi-weight extension, we extend the idea of single shared weight WANNs to multiple weights to enable efficient continual learning. Our results across four different tasks implemented on Raspberry Pi demonstrate that the multi-weight WANNs achieve higher reward as compared to single shared weight WANNs and can be fine-tuned on device in orders of magnitude smaller time. In the multi-objective extension of WANNs, we extend the idea of a single reward function to multiple competing rewards and demonstrate sensitive and monotonic trade-off between multiple competing rewards to enable efficient configurable learning. We also demonstrate robustness of WANNs for changes in the weight parameters and changes in the environmental conditions as compared to the Proximal Policy Optimization(PPO) algorithm. These significant results open the possibility of truly autonomous agents using WANNs on low compute and power budget devices.