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A Fully Decentralized Online Actor-Critic Algorithm for Constrained Multiagent Reinforcement Learning Over Directed
IEEE Transactions on Cybernetics
|August 5, 2026
Summary
This study introduces a decentralized algorithm for constrained multiagent reinforcement learning (C-MARL) over directed graphs. The method enables agents to learn cooperatively while satisfying constraints, demonstrating effectiveness in simulations.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Control Theory
Background:
- Multiagent reinforcement learning (MARL) involves multiple agents learning to achieve a common goal.
- Constrained MARL (C-MARL) adds the complexity of satisfying system-wide constraints during cooperative learning.
- Decentralized learning is crucial for scalability but faces challenges with communication and coordination.
Purpose of the Study:
- To develop a fully decentralized online algorithm for C-MARL over fixed directed graphs.
- To enable agents to cooperatively optimize a global objective while adhering to global constraints.
- To address challenges posed by row-stochastic communication matrices in decentralized settings.
Main Methods:
- Proposed a fully decentralized online actor-critic (AC) algorithm.
- Incorporated Perron-vector normalization to handle communication matrix properties.
- Agents update parameters using only local information from in-neighbors.
- Established convergence guarantees using linear function approximation for the critic.
Main Results:
- The proposed algorithm effectively handles decentralized learning in C-MARL.
- Perron-vector normalization facilitates stable learning over directed graphs.
- Convergence is guaranteed under specific function approximation conditions.
- Numerical simulations validated the algorithm's performance and robustness.
Conclusions:
- The developed decentralized AC algorithm is effective for C-MARL problems.
- The approach offers a scalable solution for cooperative learning with constraints.
- Perron-vector normalization is a key component for decentralized learning stability.
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