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Meng Xu

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

A Unified Self-Regulating Training Framework for Federated Deep Reinforcement Learning

Meng Xu
Xinhong Chen
Zhongying Chen
Guanyi Zhao
Yang Jin
Jianping Wang

Federated Deep Reinforcement Learning (FDRL) aims to enable distributed collaborative training of multiple DRL models while preserving privacy. Existing FDRL methods function in static client environments, but real-world scenarios often involve dynamic state transitions, such as noise, which render static model topologies inadequate and result in biased policy loss. This degrades client performance and leads to suboptimal global policies. To address this challenge, we develop a generic solution, referred to as the self-regulating training framework, which can be seamlessly integrated into existing FDRL approaches to address dynamic state transitions. Specifically, we propose a Sparse Training (ST) method that dynamically sparsifies and adjusts the topology of each model during training to maximize model performance and reduce model complexity. Additionally, we introduce an auxiliary model to adaptively regulate the policy loss of client models, mitigating loss bias and facilitating updates that yield improved returns. Experimental results demonstrate that our method enhances six state-of-the-art (SOTA) FDRL approaches across nine tasks in terms of return.

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AAAI Conference 2025 Conference Paper

Causally Consistent Normalizing Flow

Qingyang Zhou
Kangjie Lu
Meng Xu

Causal inconsistency arises when the underlying causal graphs captured by generative models like Normalizing Flows are inconsistent with those specified in causal models like Struct Causal Models. This inconsistency can cause unwanted issues including unfairness. Prior works to achieve causal consistency inevitably compromise the expressiveness of their models by disallowing hidden layers. In this work, we introduce a new approach: Causally Consistent Normalizing Flow (CCNF). To the best of our knowledge, CCNF is the first causally consistent generative model that can approximate any distribution with multiple layers. CCNF relies on two novel constructs: a sequential representation of SCMs and partial causal transformations. These constructs allow CCNF to inherently maintain causal consistency without sacrificing expressiveness. CCNF can handle all forms of causal inference tasks, including interventions and counterfactuals. Through experiments, we show that CCNF outperforms current approaches in causal inference. We also empirically validate the practical utility of CCNF by applying it to real-world datasets and show how CCNF addresses challenges like unfairness effectively.

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ICLR Conference 2024 Conference Paper

CAMBranch: Contrastive Learning with Augmented MILPs for Branching

Jiacheng Lin
Meng Xu
Zhihua Xiong
Huangang Wang

Recent advancements have introduced machine learning frameworks to enhance the Branch and Bound (B\&B) branching policies for solving Mixed Integer Linear Programming (MILP). These methods, primarily relying on imitation learning of Strong Branching, have shown superior performance. However, collecting expert samples for imitation learning, particularly for Strong Branching, is a time-consuming endeavor. To address this challenge, we propose \textbf{C}ontrastive Learning with \textbf{A}ugmented \textbf{M}ILPs for \textbf{Branch}ing (CAMBranch), a framework that generates Augmented MILPs (AMILPs) by applying variable shifting to limited expert data from their original MILPs. This approach enables the acquisition of a considerable number of labeled expert samples. CAMBranch leverages both MILPs and AMILPs for imitation learning and employs contrastive learning to enhance the model's ability to capture MILP features, thereby improving the quality of branching decisions. Experimental results demonstrate that CAMBranch, trained with only 10\% of the complete dataset, exhibits superior performance. Ablation studies further validate the effectiveness of our method.

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TIST Journal 2024 Journal Article

Strengthening Cooperative Consensus in Multi-Robot Confrontation

Meng Xu
Xinhong Chen
Yechao She
Yang Jin
Guanyi Zhao
Jianping Wang

Multi-agent reinforcement learning (MARL) has proven effective in training multi-robot confrontation, such as StarCraft and robot soccer games. However, the current joint action policies utilized in MARL have been unsuccessful in recognizing and preventing actions that often lead to failures on our side. This exacerbates the cooperation dilemma, ultimately resulting in our agents acting independently and being defeated individually by their opponents. To tackle this challenge, we propose a novel joint action policy, referred to as the consensus action policy (CAP). Specifically, CAP records the number of times each joint action has caused our side to fail in the past and computes a cooperation tendency, which is integrated with each agent’s Q -value and Nash bargaining solution to determine a joint action. The cooperation tendency promotes team cooperation by selecting joint actions that have a high tendency of cooperation and avoiding actions that may lead to team failure. Moreover, the proposed CAP policy can be extended to partially observable scenarios by combining it with Deep Q network or actor-critic–based methods. We conducted extensive experiments to compare the proposed method with seven existing joint action policies, including four commonly used methods and three state-of-the-art methods, in terms of episode rewards, winning rates, and other metrics. Our results demonstrate that this approach holds great promise for multi-robot confrontation scenarios.

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TIST Journal 2023 Journal Article

Deep Reinforcement Learning for Parameter Tuning of Robot Visual Servoing

Meng Xu
Jianping Wang

Robot visual servoing controls the motion of a robot through real-time visual observations. Kinematics is a key approach to achieving visual servoing. One key challenge of kinematics-based visual servoing is that it requires time-varying parameter configuration throughout the entire process of one task. Parameter tuning is also necessary when applying to different tasks. The existing work on parameter tuning either lacks adaptation or cannot automate the tuning of all parameters. Meanwhile, the transferability of existing methods from one task to another is low. This work develops a Deep Reinforcement Learning (DRL) framework for robot visual servoing, which can automate all parameters tuning for one task and across tasks. In visual servoing, forward kinematics focuses on motion speed, while inverse kinematics focuses on the smoothness of motion. Therefore, we develop two separate modules in the proposed DRL framework. One tunes time-varying Forward Kinematics parameters to accelerate the motion, and the other tunes the Inverse Kinematics parameters to ensure smoothness. Moreover, we customize a knowledge transfer method to generalize the proposed DRL models to various robot tasks without reconstructing the neural network. We verify the proposed method on simulated robot tasks. The experimental results show that the proposed method outperforms the state-of-the-art methods and manual parameter configuration in terms of movement speed and smoothness in one task and across tasks.

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TIST Journal 2023 Journal Article

Dynamic Weights and Prior Reward in Policy Fusion for Compound Agent Learning

Meng Xu
Yechao She
Yang Jin
Jianping Wang

In Deep Reinforcement Learning (DRL) domain, a compound learning task is often decomposed into several sub-tasks in a divide-and-conquer manner, each trained separately and then fused concurrently to achieve the original task, referred to as policy fusion. However, the state-of-the-art (SOTA) policy fusion methods treat the importance of sub-tasks equally throughout the task process, eliminating the possibility of the agent relying on different sub-tasks at various stages. To address this limitation, we propose a generic policy fusion approach, referred to as Policy Fusion Learning with Dynamic Weights and Prior Reward (PFLDWPR), to automate the time-varying selection of sub-tasks. Specifically, PFLDWPR produces a time-varying one-hot vector for sub-tasks to dynamically select a suitable sub-task and mask the rest throughout the entire task process, enabling the fused strategy to optimally guide the agent in executing the compound task. The sub-tasks with the dynamic one-hot vector are then aggregated to obtain the action policy for the original task. Moreover, we collect sub-tasks’s rewards at the pre-training stage as a prior reward, which, along with the current reward, is used to train the policy fusion network. Thus, this approach reduces fusion bias by leveraging prior experience. Experimental results under three popular learning tasks demonstrate that the proposed method significantly improves three SOTA policy fusion methods in terms of task duration, episode reward, and score difference.

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EAAI Journal 2022 Journal Article

InterpolationSLAM: An effective visual SLAM system based on interpolation network

Zhenkun Zhu
Jikai Wang
Meng Xu
Shiqi Lin
Zonghai Chen

Visual SLAM can be mainly divided into direct method and feature-based method, and these two methods develop relatively independently. In recent years, feature-based SLAM systems have been significantly improved by introducing more robust features, effective matching and optimization frameworks, or other sensors. The introduction of semantic information also promotes the development of direct methods. However, visual SLAM usually assumes that the system satisfies the constant velocity assumption. This assumption may lead to a poor initial pose so that the subsequent optimization falls into a local minimal. Meanwhile, large field of view changes often lead to an increase in the error of feature matching for feature-based method and large illumination changes often lead to an increase of photometric error for direct method, thus negatively influencing SLAM systems. In this paper, we mainly target at feature-based method. In detail, we focus on the number and quality of feature matches, as well as the accuracy of the initial pose, thus an interpolation mechanism for SLAM is proposed. Specifically, we introduce the interpolation network originally used to increase the number of video frames into visual SLAM. First, we point out that the traditional interpolation network evaluation metrics are not suitable for the SLAM systems, and we provide the corresponding evaluation metric. Secondly, we verify that it works both for hand-crafted and deep learning features. Thirdly, in order to verify the effectiveness and transferability of our method, we also apply our method to SLAM systems based on direct method, which proves that our method is also applicable to the direct method. Fourthly, we point out that the interpolation network effectively slows down the pose transformation of the SLAM system by inserting an intermediate frame between the previous frame and the current frame, so that the system can obtain a better initial pose based on the constant velocity assumption. This can also explain why visual-inertial systems can effectively improve the performance of visual SLAM. Finally, to ensure the efficiency of the SLAM system, we provide a turning detection module and propose a method to interpolate only at turnings. Extensive experiments and analyses verify the effectiveness and transferability of the proposed system.

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