Centralized Training with Decentralized Execution

Also known as CTDE

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8 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Position: Beyond Training Time, Test-Time Coordination is Essential for Cooperative MARL
Oct 12, 2026Dongsu Lee, Sooraj Sathish, Joonkyung Kim +2Centralized Training with Decentralized Execution Cooperative MARL
The University of Texas at Austin · Texas A&M University · KAIST
MA-JEPA: Joint-Embedding World Models for Multi-Agent Reinforcement Learning
Sep 27, 2026Brandon Gary Kaplowitz, Osaze James Obahor, Christian Schroeder de WittGame-Playing Agents World Model Learning
Department of Engineering Science, University of Oxford
Efficient Real-World Online Reinforcement Learning for Robot Manipulation via Centralized Training and Critic Decomposition
Aug 10, 2026Changhao Li, Yifang Zhang, Heng Zhang +6Reinforcement Learning Robotic RL
HHCM, Istituto Italiano di Tecnologia, Genoa, Italy · DIBRIS, University of Genova, Genova, Italy · Human-Robot Interfaces and Interaction Lab, Istituto Italiano di Tecnologia, Genova, Italy +1
Distilling Collaborative Dynamics into Latent Space for Implicit Coordination in Decentralized Multi-Agent Manipulation
Jun 22, 2026Chanyoung Park, Minsung Yoon, Andrew Jeong +1Multi-Agent Coordination Centralized Training with Decentralized Execution
School of Computing at the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
A Cross-Layered Multi-Drone Coordination for Medical Supply Delivery during Disaster Response Management
May 10, 2026Aneesh Calyam, Subrahmanya Chandra Bhamidipati, Zack Murry +1Aerial Robotics UAV Navigation
University of Missouri–Columbia, Columbia, MO 65211, USA
Rethinking Ratio-Based Trust Regions for Policy Optimization in Multi-Agent Reinforcement Learning
May 9, 2026Chulabhaya Wijesundara, Andrea Baisero, Zhongheng Li +3Multi-Agent System Optimization Policy Optimization
Northeastern University · STR · University of California, Irvine
COIN: Collaborative Interaction-Aware Multi-Agent Reinforcement Learning for Self-Driving Systems
Mar 26, 2026Yifeng Zhang, Jieming Chen, Tingguang Zhou +4Centralized Training with Decentralized Execution Multi-Agent Coordination
Department of Mechanical Engineering, College of Design and Engineering, National University of Singapore, 21 Lower Kent Ridge Rd, 119077, Singapore · Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, 100872, China · School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China
A Robust and Efficient Multi-Agent Reinforcement Learning Framework for Traffic Signal Control
Mar 12, 2026Sheng-You Huang, Hsiao-Chuan Chang, Yen-Chi Chen +6Centralized Training with Decentralized Execution Reinforcement Learning
National Yang Ming Chiao Tung University · Academia Sinica · Kochi University of Technology +1

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Position: Beyond Training Time, Test-Time Coordination is Essential for Cooperative MARL

Dongsu Lee, Sooraj Sathish, Joonkyung Kim, Woojun Kim+1

The University of Texas at Austin · Texas A&M University · KAIST

Centralized Training with Decentralized Execution Cooperative MARL Multi-Agent Coordination

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We argue that cooperative multi-agent reinforcement learning (MARL) should treat test-time coordination as part of the problem definition rather than a deployment artifact. Our focus is on long-horizon, centrally monitored cooperative systems such as warehouse robotics, automated storage-and-retrieval systems, and cloud-resource scheduling, where teams share a reward, operate under shifting objectives, and already run with global telemetry at deployment. The dominant paradigm in this regime, centralized training with decentralized execution (CTDE), uses global information only during training and provides no coordination channel at execution. This is sufficient when agents are weakly coupled or the environment is stationary, but it fails when the team must realign after conditions change. The limitation is structural rather than algorithmic, and more training cannot add a channel that does not exist at execution time. We call this the recoverability gap. To address it, we name the underexplored design space of intermittent test-time coordination as centralized training with mixed execution (CTME), in which a coordinator observes global state and emits high-level signals while agents act locally. CTME is not a particular learning algorithm, but a cooperative MARL problem setting that makes test-time information structure explicit. It specifies team-level information, high-level signals, and update frequency after training, with CTDE recovered as a limit case. We do not argue that CTDE is obsolete. We argue that removing the test-time channel is a modeling choice, not a necessity, and the wrong default when global telemetry is available and teams must recover from drift.