Before revision: We propose Learning Off-Policy with Online Planning (LOOP) , an efficient reinforcement learning framework, combining the benefits of model-based local trajectory optimization and off-policy algorithms. The agent learns a dynamics model and then uses trajectory optimization with the learned model to select actions.
After revision: We propose Learning Off-Policy with Online Planning (LOOP) , an efficient reinforcement learning framework, combining the benefits of model-based local trajectory optimization and risk-sensitive domains requires performant and flexible deployment policies that can readily incorporate constraints during deployment. One such class of policies are the semi-parametric H-step lookahead policies, which select actions using trajectory optimization over a dynamics model and then uses trajectory optimization with the learned model to select actions.