Uber Technologies, Inc.
DIFFERENTIABLE NEUROMODULATED PLASTICITY FOR REINFORCEMENT LEARNING AND SUPERVISED LEARNING TASKS

Abstract:

A system uses neural networks for applications such as navigation of autonomous vehicles or mobile robots. The system uses a trained neural network model that comprises fixed parameters that remain unchanged during execution of the model, plastic parameters that are modified during execution of the model, and nodes that generate outputs based on the inputs, fixed parameters, and the plastic parameters. The system provides input data to the neural network model and executes the neural network model. The system updates the plastic parameters of the neural network model by adjusting the rate at which the plastic parameters update over time based on at least one output of a node.