EfficientTDMPC: Improved MPC Objectives for Sample-Efficient Continuous Control
TL;DR
EfficientTDMPC is introduced, a sample-efficient model-based reinforcement learning method for continuous control built on the TD-MPC family of algorithms that is the new state of the art in sample efficiency on HumanoidBench and the DeepMind Control Suite.
Abstract
We introduce EfficientTDMPC, a sample-efficient model-based reinforcement learning method for continuous control built on the TD-MPC family of algorithms. Central to this family is a planner that aims to find an action sequence that maximizes the estimated return. The return is estimated using a learned model and value networks, each of which can introduce error. EfficientTDMPC introduces three contributions that improve performance by aiming to reduce this error. First, we introduce a multi-horizon planning objective that evaluates the value at different rollout depths and averages them. Second, to our knowledge we are the first to train a value-equivalent dynamics ensemble. Our improved objective then averages over rollouts from multiple dynamics heads. Third, we add pessimistic reanalyze for tasks that can terminate early. Applying our contributions to a recent baseline (BMPC) yields EfficientTDMPC, which to our knowledge is the new state of the art in sample efficiency on HumanoidBench and the DeepMind Control Suite, reaching BMPC's final aggregated performance using 57\% fewer environment steps.
