Reward Machines

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8 papers0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

RM-VLM: From Natural-Language Task Descriptions to Vision-Grounded Reward Machines
Nov 5, 2026Roko Parac, Christos Ziakas, Leo Ardon +1Reward Machines Reinforcement Learning
Imperial College London, London, United Kingdom
Reward Machines for Signal Temporal Logic
Aug 13, 2026Alper Kamil Bozkurt, Shangtong Zhang, Yuichi MotaiReward Machines Reward Design for RL
Department of Computer Science, University of Virginia, Charlottesville, VA, USA
Reward Structure Shapes the Interaction Between Episodic Exploration and Neural Memory in Reinforcement Learning
Aug 5, 2026Jai Malegaonkar, Rohan Patil, Henrik I. ChristensenReward Machines Reinforcement Learning
Department of Computer Science and Engineering, UC San Diego, United States
Pareto Q-Learning with Reward Machines
Jun 17, 2026Arnaud Lequen, Clément Legrand-Lixon, Léo SaulièresMulti-Objective Reinforcement Learning Reward Machines
Linköping University, Sweden · Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStal, F-59000 Lille, France · Univ. Toulouse, INRAE-MIAT, Toulouse, France
Safe Reinforcement Learning of Autonomous Highway Driving: A Unified Framework for Safety and Efficiency
Jun 12, 2026Chufei Yan, Zhihao Cui, Yiyan Lv +3Reward Machines Reinforcement Learning
School of Physics, Northeast Normal University, Changchun, 130024, China. · 2Clean Energy Automotive Engineering Center, School of Automotive Studies, Tongji University, Shanghai, 201804, China. · 3Mengshi Automobile Technology Company, Dongfeng Motor Corporation, Wuhan, 430010, China.
On Tackling Complex Tasks with Reward Machines and Signal Temporal Logics
Apr 9, 2026Ana María Gómez Ruiz, Thao Dang, Alexandre DonzéReward Machines RL Control
UGA · VERIMAG, Université Grenoble Alpes · VERIMAG - IMAG, CNRS, UGA +1
Reinforcement Learning with Symbolic Reward Machines
Mar 3, 2026Thomas Krug, Daniel NeiderReward Machines Non-Markovian RL
TU Dortmund University · Research Center Trustworthy Data Science and Security
Reinforcement Learning for Long-Horizon Unordered Tasks: From Boolean to Coupled Reward Machines
Oct 31, 2025Kristina Levina, Nikolaos Pappas, Athanasios Karapantelakis +2Reward Machines Reinforcement Learning
Linköping University, Linköping, Sweden · Ericsson Research, Stockholm, Sweden

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RM-VLM: From Natural-Language Task Descriptions to Vision-Grounded Reward Machines

Roko Parac, Christos Ziakas, Leo Ardon, Alessandra Russo

Imperial College London, London, United Kingdom

Reward Machines Reinforcement Learning Multimodal Reward Modeling Non-Markovian RL LLM-Guided RL VLMs for Robotics

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Reward machines (RMs) have been proposed as a reward formulation in reinforcement learning (RL) to capture non-Markovian tasks where credit assignment depends on history. By representing reward functions as automata, RMs expose the task's temporal structure and allow such rewards to be handled within a Markovian framework. However, they assume a labelling function, a proposition-grounding oracle for observations, which must be manually engineered for each environment, limiting their wider applicability. In this work, we address this limitation by proposing RM-VLM, a novel approach in which a large language model (LLM) generates an RM from a natural-language task description, and a vision-language model (VLM) acts as the labelling function, grounding the RM's propositions over image-based observations during RM-guided policy learning. A VLM-based value function estimator is used to assess the agent's progress towards each RM transition and derive a dense reward signal. We evaluate RM-VLM on three domains covering non-Markovian grid-world navigation, robotic manipulation, and 3D object collection. Our experimental results show that RM-VLM consistently achieves higher task success rates than existing VLM-based reward methods across all tested domains.