Evaluating Model-Based Planning and Planner Amortization for Continuous Control

There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this intuition on various challenging locomotion tasks. We take a hybrid approach, combining model predictive control (MPC) with a learned model and model-free policy learning; the learned policy serves as a proposal for MPC. We show that MPC with learned proposals and models (trained on the fly or transferred from related tasks) can significantly improve performance and data efficiency with respect to model-free methods. However, we find that well-tuned model-free agents are strong baselines even for high DoF control problems. Finally, we show that it is possible to distil a model-based planner into a policy that amortizes the planning computation without any loss of performance.

Authors

• Arunkumar Byravan
• Leonard Hasenclever
• Piotr Trochim
• Mehdi Mirza
• Alessandro Davide Ialongo
• Yuval Tassa
• Jost Tobias Springenberg
• Abbas Abdolmaleki
• Nicolas Heess
• Josh Merel
• Martin A. Riedmiller

Keywords

• Model-based Reinforcement Learning
• Planning
• Robotics
• Model Predictive Control
• Learning