Before revision:  The agent learns a dynamics model and then uses trajectory optimization with the learned model to select actions. To sidestep the myopic effect of fixed-horizon trajectory optimization, a value function learned through an off-policy algorithm is attached to the end of the planning horizon.
After revision:  The agent learns a dynamics model for a fixed horizon with a terminal value function. In this work, we investigate a novel instantiation of H-step lookahead with a learned model and a terminal value function learned through an off-policy algorithm is attached to the end of the planning horizon.