Methods and systems for training a neural network using sequential monte carlo processing

Abstract

A computer-implemented method, a machine learning system, and non-transitory computer-readable storage medium for training a neural network are provided. The neural network is used to instruct an agent to select actions for interacting with an environment to determine a solution to a specified problem. In the computer-implemented method a state signal representing a current state of the environment is received. A Sequential Monte Carlo process is then used to perform a search to determine target action selection data associated with the current state of the environment. This target action selection data is stored in association with the state signal and the current state of the environment is updated by providing an action selection signal based on the target action selection data. The Sequential Monte Carlo process involves generating a plurality of simulations using the neural network to determine the target action selection data.