Before revision:  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. We investigate various instantiations of this framework and demonstrate its benefit in three settings: online reinforcement learning, offline reinforcement learning, and safe learning. We show that this method significantly improves the underlying model-based and model-free algorithms and achieves state-of-the-art performancein a variety of settings.
After revision:  To sidestep the myopic effect of fixed-horizon trajectory optimization, a value function learned through an off-policy algorithm , named Learning Off-Policy with Online Planning (LOOP). We provide a theoretical analysis of this method, suggesting a tradeoff between model errors and value function errors and empirically demonstrate this tradeoff to be beneficial in deep reinforcement learning. Furthermore, we identify the "Actor Divergence" issue in this framework and propose Actor Regularized Control (ARC), a modified trajectory optimization procedure. We evaluate our method on a set of robotic tasks for Offline and Online RL and demonstrate improved performance. We also show the flexibility of LOOP to incorporate safety constraints during deployment with a set of navigation environments. We demonstrate that LOOP is a desirable framework for robotics applications based on its strong performance in various important RL settings.