Biao Luo

AAAI Conference 2026 Conference Paper

Autonomous Partner Selection for Cooperative Multi-Agent Reinforcement Learning

• Rui Tang
• Biao Luo
• Yongzheng Cui

In cooperative Multi-Agent Reinforcement Learning (MARL), the subgroup-wise learning is employed to assign sub-tasks to agents towards the enhancement of team collaboration. However, the present work is dependent on manually defined allocation criteria, which hinders its capacity to adapt to environmental changes promptly, and also relaxes communication restrictions, thereby constraining the application of algorithms in a range of fields. In order to address these issues, the Autonomous Partner Selection (APS) framework is proposed, which offers an implicit grouping mechanism in an autonomous way. Each agent is capable of autonomously selecting cooperative partners and integrating their own observations with those of partners to harmonise the cooperative behaviour during the training stage. With a view to strictly restricting communication, the intention encoder is trained through information distillation, which enables agents to selectively take more cooperative actions based solely on local observations. Meanwhile, in order to circumvent potential conflicts engendered by homogenization behaviour, we employ a contrastive learning strategy to the cooperative intention generated by agents, thereby ensuring that the behavioural tendencies exhibited by different individuals remain as diverse as possible. Finally, extensive comparative experiments on the StarCraft Multi-Agent Challenge and Google Research Football are conducted. The results demonstrate that APS exhibits superior performance in comparison to the state-of-the-art algorithms across a range of tasks, and agents can adapt their grouping strategies in accordance with the environment to facilitate enhanced cooperation.

AAAI Conference 2026 Conference Paper

Beyond Monotonicity: Revisiting Factorization Principles in Multi-Agent Q-Learning

• Tianmeng Hu
• Yongzheng Cui
• Rui Tang
• Biao Luo
• Ke Li

Value decomposition is a central approach in multi-agent reinforcement learning (MARL), enabling centralized training with decentralized execution by factorizing the global value function into local values. To ensure individual-global-max (IGM) consistency, existing methods either enforce monotonicity constraints, which limit expressive power, or adopt softer surrogates at the cost of algorithmic complexity. In this work, we present a dynamical systems analysis of non-monotonic value decomposition, modeling learning dynamics as continuous-time gradient flow. We prove that, under approximately greedy exploration, all zero-loss equilibria violating IGM consistency are unstable saddle points, while only IGM-consistent solutions are stable attractors of the learning dynamics. Extensive experiments on both synthetic matrix games and challenging MARL benchmarks demonstrate that unconstrained, non-monotonic factorization reliably recovers IGM-optimal solutions and consistently outperforms monotonic baselines. Additionally, we investigate the influence of temporal-difference targets and exploration strategies, providing actionable insights for the design of future value-based MARL algorithms.

EAAI Journal 2025 Journal Article

Adaptive neural boundary control for multi-agent manipulators system with uncertainties through cooperative disturbance observers network

• Zhibo Zhao
• Yuan Yuan
• Xiaodong Xu
• Biao Luo
• Tingwen Huang

AAAI Conference 2024 Conference Paper

PA2D-MORL: Pareto Ascent Directional Decomposition Based Multi-Objective Reinforcement Learning

• Tianmeng Hu
• Biao Luo

Multi-objective reinforcement learning (MORL) provides an effective solution for decision-making problems involving conflicting objectives. However, achieving high-quality approximations to the Pareto policy set remains challenging, especially in complex tasks with continuous or high-dimensional state-action space. In this paper, we propose the Pareto Ascent Directional Decomposition based Multi-Objective Reinforcement Learning (PA2D-MORL) method, which constructs an efficient scheme for multi-objective problem decomposition and policy improvement, leading to a superior approximation of Pareto policy set. The proposed method leverages Pareto ascent direction to select the scalarization weights and computes the multi-objective policy gradient, which determines the policy optimization direction and ensures joint improvement on all objectives. Meanwhile, multiple policies are selectively optimized under an evolutionary framework to approximate the Pareto frontier from different directions. Additionally, a Pareto adaptive fine-tuning approach is applied to enhance the density and spread of the Pareto frontier approximation. Experiments on various multi-objective robot control tasks show that the proposed method clearly outperforms the current state-of-the-art algorithm in terms of both quality and stability of the outcomes.