Robust reinforcement learning for continuous control with model misspecification

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a policy neural network having policy parameters. One of the methods includes sampling a mini-batch comprising one or more observation-action-reward tuples generated as a result of interactions of a first agent with a first environment; determining an update to current values of the Q network parameters by minimizing a robust entropy-regularized temporal difference (TD) error that accounts for possible perturbations of the states of the first environment represented by the observations in the observation-action-reward tuples; and determining, using the Q-value neural network, an update to the policy network parameters using the sampled mini-batch of observation-action-reward tuples.