Hybrid agent for parameter optimization using prediction and reinforcement learning

Abstract

A computer-implemented method for reinforcement learning includes obtaining a first observation ot-1 of an environment at an end of a first time step t-1, generating a prediction o′t of a second observation ot of the environment at the end of a second time step t based on at least the first observation, obtaining a predicted state s′t of the environment at the second time step t from the predicted second observation o′t, selecting an action at to execute on the environment during the second time step based on the predicted state s′t and a policy π, and executing the action at on the environment.

Dependencies are inferred from the claim text.