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Lujuan Dang

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3 papers

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AAAI Conference 2026 Conference Paper

Gradient-Protected Value Decomposition for Cooperative Multi-Agent Reinforcement Learning

Jie Hou
Haowen Dou
Lujuan Dang
Liangjun Chen
Chenyang Ge

In recent years, deep multi-agent reinforcement learning (MARL) has demonstrated remarkable potential in solving complex cooperative tasks by enabling decentralized yet efficient coordination among agents. However, during decentralized training, agent policy updates induced by different joint action samples may conflict, leading to gradient interference that hinders convergence and the emergence of coordinated behavior. In this paper, we analyze and empirically validate the phenomenon of gradient interference. To address this, we then propose Gradient-Protected Value Decomposition (GPVD), a novel MARL framework that explicitly protects the gradient signals of optimal collaborative actions by suppressing the impact of interfering actions. GPVD employs a dynamic gradient protection mechanism that identifies optimal collaborative joint actions and reweights the loss to attenuate gradients from non-collaborative interfering actions. To effectively identify high-value collaborative actions, we apply SimHash-based state grouping to discover consistent collaboration patterns across similar states. Furthermore, a count-based intrinsic reward is incorporated to encourage exploration and improve the coverage of potentially optimal joint actions. Experiments on challenging multi-agent benchmarks demonstrate that GPVD achieves faster convergence, stronger coordination, and greater training stability compared to state-of-the-art value decomposition methods.

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YNIMG Journal 2024 Journal Article

BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping

Kaizhong Zheng
Shujian Yu
Liangjun Chen
Lujuan Dang
Badong Chen

Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms and divergent responses to treatment. This clinical heterogeneity has hindered the progress of precision diagnosis and treatment effectiveness in psychiatric disorders. In this study, we propose BPI-GNN, a new interpretable graph neural network (GNN) framework for analyzing functional magnetic resonance images (fMRI), by leveraging the famed prototype learning. In addition, we introduce a novel generation process of prototype subgraph to discover essential edges of distinct prototypes and employ total correlation (TC) to ensure the independence of distinct prototype subgraph patterns. BPI-GNN can effectively discriminate psychiatric patients and healthy controls (HC), and identify biological meaningful subtypes of psychiatric disorders. We evaluate the performance of BPI-GNN against 11 popular brain network classification methods on three psychiatric datasets and observe that our BPI-GNN always achieves the highest diagnosis accuracy. More importantly, we examine differences in clinical symptom profiles and gene expression profiles among identified subtypes and observe that our identified brain-based subtypes have the clinical relevance. It also discovers the subtype biomarkers that align with current neuro-scientific knowledge.

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NeurIPS Conference 2024 Conference Paper

Measuring Mutual Policy Divergence for Multi-Agent Sequential Exploration

Haowen Dou
Lujuan Dang
Zhirong Luan
Badong Chen

Despite the success of Multi-Agent Reinforcement Learning (MARL) algorithms in cooperative tasks, previous works, unfortunately, face challenges in heterogeneous scenarios since they simply disable parameter sharing for agent specialization. Sequential updating scheme was thus proposed, naturally diversifying agents by encouraging agents to learn from preceding ones. However, the exploration strategy in sequential scheme has not been investigated. Benefiting from updating one-by-one, agents have the access to the information from preceding agents. Thus, in this work, we propose to exploit the preceding information to enhance exploration and heterogeneity sequentially. We present Multi-Agent Divergence Policy Optimization (MADPO), equipped with mutual policy divergence maximization framework. We quantify the policy discrepancies between episodes to enhance exploration and between agents to heterogenize agents, termed intra-agent and inter-agent policy divergence. To address the issue that traditional divergence measurements lack stability and directionality, we propose to employ the conditional Cauchy-Schwarz divergence to provide entropy-guided exploration incentives. Extensive experiments show that the proposed method outperforms state-of-the-art sequential updating approaches in two challenging multi-agent tasks with various heterogeneous scenarios. Source code is available at \url{https: //github. com/hwdou6677/MADPO}.

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