EfficientTDMPC: Improved MPC Objectives for Sample-Efficient Continuous Control

EfficientTDMPC is a sample‑efficient, model‑based reinforcement learning method for continuous control that builds on the TD‑MPC family. It reduces estimation error by using an ensemble of dynamics models and averaging return estimates across models and rollout depths, and it can penalize uncertainty in the planner objective. Practical improvements such as fresher buffer data and reduced compute enable the algorithm to benefit from a higher update‑to‑data ratio, achieving state‑of‑the‑art sample efficiency on HumanoidBench‑Hard and DMC hard while matching state‑of‑the‑art on DMC easy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• reinforcement-learning
• benchmarks