Multi-agent Guided Policy Optimization

Abstract

Due to practical constraints such as partial observability and limited communication, Centralized Training with Decentralized Execution (CTDE) has become the dominant paradigm in cooperative Multi-Agent Reinforcement Learning (MARL). However, existing CTDE methods often underutilize centralized training or lack theoretical guarantees. We propose Multi-Agent Guided Policy Optimization (MAGPO), a novel framework that better leverages centralized training by integrating centralized guidance with decentralized execution. MAGPO uses an autoregressive joint policy for scalable, coordinated exploration and explicitly aligns it with decentralized policies to ensure deployability under partial observability. We provide theoretical guarantees of monotonic policy improvement and empirically evaluate MAGPO on 43 tasks across 6 diverse environments. Results show that MAGPO consistently outperforms strong CTDE baselines and matches or surpasses fully centralized approaches, offering a principled and

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyli2025multi

Related papers

• Jointppo: Diving Deeper Into The Effectiveness Of PPO In Multi-agent Reinforcement Learning (2024)0.00
• Multi-path Policy Optimization (2019)0.00
• Is Centralized Training With Decentralized Execution Framework Centralized Enough For MARL? (2023)0.00
• Multi-agent Trust Region Policy Optimization (2020)12.61
• Co2po: Coordinated Constrained Policy Optimization For Multi-agent RL (2026)0.00
• Towards Global Optimality In Cooperative MARL With The Transformation And Distillation Framework (2022)0.00
• Model-based Decentralized Policy Optimization (2023)0.00
• Multi-agent Constrained Policy Optimisation (2021)0.00