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Zhixiao Sun

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5 papers

2 author rows

IROS Conference 2025 Conference Paper

IHGSL: Interpretable Heuristic Graph Structure Learning for Multi-Robot Autonomous Collaborative Systems

Yue Han
Hanqi Li
Cuiwei Liu
Chen Liang
Zhixiao Sun

In multi-robot systems, capturing the complex and dynamic interaction relationships is essential for enhancing autonomous collaboration. However, existing learning-based approaches usually overlook the understanding of these relationships, leading to reliability issues and hindering their application to real-world scenarios. This paper proposes a novel approach called Interpretable Heuristic Graph Structure Learning (IHGSL) to better comprehend the complex collaborative relationships in multi-robot systems. We first construct a predicate space to define diverse predicates that express fundamental relationships. Then we employ the variational information bottleneck technique to acquire a latent representation of the current observation by aligning it with the historical trajectory. On this basis, the predicates that the robot should currently focus on the most are learned, and some interaction relationships are established accordingly. Thereby an interpretable relationship graph is generated heuristically to guide the achievement of multi-robot autonomous collaborative decision-making. Through experimental evaluation, we demonstrate the process of relationship inference, thus validating the interpretability of IHGSL. Compared with existing methods, IHGSL also achieves superior collaboration performance, which highlights the effectiveness of the learned heuristic graph structure.

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TIST Journal 2024 Journal Article

Discovering Expert-Level Air Combat Knowledge via Deep Excitatory-Inhibitory Factorized Reinforcement Learning

Hai Yin Piao
Shengqi Yang
Hechang Chen
Junnan Li
Jin Yu
Xuanqi Peng
Xin Yang
Zhen Yang

Artificial Intelligence (AI) has achieved a wide range of successes in autonomous air combat decision-making recently. Previous research demonstrated that AI-enabled air combat approaches could even acquire beyond human-level capabilities. However, there remains a lack of evidence regarding two major difficulties. First, the existing methods with fixed decision intervals are mostly devoted to solving what to act but merely pay attention to when to act, which occasionally misses optimal decision opportunities. Second, the method of an expert-crafted finite maneuver library leads to a lack of tactics diversity, which is vulnerable to an opponent equipped with new tactics. In view of this, we propose a novel Deep Reinforcement Learning (DRL) and prior knowledge hybrid autonomous air combat tactics discovering algorithm, namely deep E xcitatory-i N hibitory f ACT or I zed maneu VE r ( ENACTIVE ) learning. The algorithm consists of two key modules, i.e., ENHANCE and FACTIVE. Specifically, ENHANCE learns to adjust the air combat decision-making intervals and appropriately seize key opportunities. FACTIVE factorizes maneuvers and then jointly optimizes them with significant tactics diversity increments. Extensive experimental results reveal that the proposed method outperforms state-of-the-art algorithms with a 62% winning rate and further obtains a margin of a 2.85-fold increase in terms of global tactic space coverage. It also demonstrates that a variety of discovered air combat tactics are comparable to human experts’ knowledge.

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AAAI Conference 2023 Conference Paper

The Sufficiency of Off-Policyness and Soft Clipping: PPO Is Still Insufficient according to an Off-Policy Measure

Xing Chen
Dongcui Diao
Hechang Chen
Hengshuai Yao
Haiyin Piao
Zhixiao Sun
Zhiwei Yang
Randy Goebel

The popular Proximal Policy Optimization (PPO) algorithm approximates the solution in a clipped policy space. Does there exist better policies outside of this space? By using a novel surrogate objective that employs the sigmoid function (which provides an interesting way of exploration), we found that the answer is "YES", and the better policies are in fact located very far from the clipped space. We show that PPO is insufficient in "off-policyness", according to an off-policy metric called DEON. Our algorithm explores in a much larger policy space than PPO, and it maximizes the Conservative Policy Iteration (CPI) objective better than PPO during training. To the best of our knowledge, all current PPO methods have the clipping operation and optimize in the clipped policy space. Our method is the first of this kind, which advances the understanding of CPI optimization and policy gradient methods. Code is available at https://github.com/raincchio/P3O.

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ICRA Conference 2022 Conference Paper

Multi-UAV Disaster Environment Coverage Planning with Limited-Endurance

Hongyu Song
Jincheng Yu
Jiantao Qiu
Zhixiao Sun
Kuijun Lang
Qing Luo
Yuan Shen 0001
Yu Wang 0002

Disaster areas involving floods and earthquakes are commonly large, with the rescue time being quite tight, suggesting multi-Unmanned Aerial Vehicles (UAV) exploration rather than employing a single UAV. For such scenarios, current UAV exploration is modeled as a Coverage Path Planning (CPP) problem to achieve full area coverage in the presence of obstacles. However, the UAV's endurance capability is limited, and the rescue time is constrained, prohibiting even multiple UAVs from completing disaster area coverage on time. Therefore, this paper defines a multi-Agent Endurance-limited CPP (MAEl-CPP) problem that is based on an a priori known heatmap of the disaster area, which affords to explore the most valuable areas under UAV limited energy constraints. Furthermore, we propose a path planning algorithm for the MAEl-CPP problem by ranking the possible disaster areas according to their importance through satellite or remote sensing aerial images and completing path planning according to this ranking. Experimental results demonstrate that the search efficiency of the proposed algorithm is 4. 2 times that of the existing algorithm.

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EAAI Journal 2021 Journal Article

Multi-agent hierarchical policy gradient for Air Combat Tactics emergence via self-play

Zhixiao Sun
Haiyin Piao
Zhen Yang
Yiyang Zhao
Guang Zhan
Deyun Zhou
Guanglei Meng
Hechang Chen

Air-to-air confrontation has attracted wide attention from artificial intelligence scholars. However, in the complex air combat process, operational strategy selection depends heavily on aviation expert knowledge, which is usually expensive and difficult to obtain. Moreover, it is challenging to select optimal action sequences efficiently and accurately with existing methods, due to the high complexity of action selection when involving hybrid actions, e. g. , discrete/continuous actions. In view of this, we propose a novel Multi-Agent Hierarchical Policy Gradient algorithm (MAHPG), which is capable of learning various strategies and transcending expert cognition by adversarial self-play learning. Besides, a hierarchical decision network is adopted to deal with the complicated and hybrid actions. It has a hierarchical decision-making ability similar to humankind, and thus, reduces the action ambiguity efficiently. Extensive experimental results demonstrate that the MAHPG outperforms the state-of-the-art air combat methods in terms of both defense and offense ability. Notably, it is discovered that the MAHPG has the ability of Air Combat Tactics Interplay Adaptation, and new operational strategies emerged that surpass the level of experts.

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