mlsanityData to 14 Oct 2026

Model-Based Data-Efficient and Robust Reinforcement Learning

arXiv 2602.00630 ↗First posted 31 Jan 2026eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

A data-efficient learning-based control design method is proposed in this paper. It is based on learning a system dynamics model that is then leveraged in a two-level procedure. On the higher level, a simple but powerful optimization procedure is performed such that, for example, energy consumption in a vehicle can be reduced when hard state and action constraints are also introduced. Load disturbances and model errors are compensated for by a feedback controller on the lower level. In that regard, we briefly examine the robustness of both model-free and model-based learning approaches, and it is shown that the model-free approach greatly suffers from the inclusion of unmodeled dynamics. In evaluating the proposed method, it is assumed that a path is given, while the velocity and acceleration can be modified such that energy is saved, while still keeping speed limits and completion time. Compared with two well-known actor-critic reinforcement learning strategies, the suggested learning-based approach saves more energy and reduces the number of evaluated time steps by a factor of 100 or more.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 30 references that are in the map. First posted on arXiv 31 Jan 2026.

Builds on

5 of 30 references.

References · 5

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  3. 2016Concrete Problems in AI Safetycited by 1,642
  4. 2018The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpointcited by 40
  5. 2020Computationally efficient algorithm for eco-driving over long look-ahead horizonscited by 1

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  3. 2025Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents
  4. 2025Learning State-Action Control Barrier Functions for Model-Free Control under Constraints
  5. 2021Two-step reinforcement learning for model-free redesign of nonlinear optimal regulator
  6. 2026An Actor-Critic-Identifier Control Design for Increasing Energy Efficiency of Automated Electric Vehicles
  7. 2026A Pontryagin Method of Model-based Reinforcement Learning via Hamiltonian Actor-Critic
  8. 2024Should we use model-free or model-based control? A case study of battery management systems