The agent is being trained by collecting a batch of experiences from the environment in the form of (a t , s t , R t ) at time t and the policy parameters are updated by computing the proximal policy optimization (PPO) loss for N off-line update steps. Advantages A t are estimated by the critic Q θ such that A t = Q θ (s t , a t ) -R t with R t having been normalized to 0 mean and standard deviation of 1 for each batch.