Tackling Uncertainties In Multi-agent Reinforcement Learning Through Integration Of Agent Termination Dynamics

Abstract

Multi-Agent Reinforcement Learning (MARL) has gained significant traction for solving complex real-world tasks, but the inherent stochasticity and uncertainty in these environments pose substantial challenges to efficient and robust policy learning. While Distributional Reinforcement Learning has been successfully applied in single-agent settings to address risk and uncertainty, its application in MARL is substantially limited. In this work, we propose a novel approach that integrates distributional learning with a safety-focused loss function to improve convergence in cooperative MARL tasks. Specifically, we introduce a Barrier Function based loss that leverages safety metrics, identified from inherent faults in the system, into the policy learning process. This additional loss term helps mitigate risks and encourages safer exploration during the early stages of training. We evaluate our method in the StarCraft II micromanagement benchmark, where our approach demonstrates improved con

Stats

• citations1
• S2 citations—
• github stars0
• HF likes0
• heat score2.26
• arxiv keyhazra2025tackling

Related papers

• Breaking The Curse Of Multiagency In Robust Multi-agent Reinforcement Learning (2024)0.00
• Risk-aware Distributed Multi-agent Reinforcement Learning (2023)3.58
• Robust Multi-agent Reinforcement Learning With State Uncertainty (2023)0.00
• Safe Multi-agent Reinforcement Learning With Convergence To Generalized Nash Equilibrium (2024)0.00
• Toward Risk-based Optimistic Exploration For Cooperative Multi-agent Reinforcement Learning (2023)0.00
• Enhancing Sample Efficiency In Multi-agent RL With Uncertainty Quantification And Selective Exploration (2025)0.00
• Multi-agent Reinforcement Learning In Stochastic Networked Systems (2020)0.00
• Policy Distillation And Value Matching In Multiagent Reinforcement Learning (2019)10.48