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Hyeoksoo Lee, Jiwoo Hong and Jongpil Jeong
The simple and labor-intensive tasks of workers on the job site are rapidly becoming digital. In the work environment of logistics warehouses and manufacturing plants, moving goods to a designated place is a typical labor-intensive task for workers. Thes...
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Simone Parisi, Davide Tateo, Maximilian Hensel, Carlo D?Eramo, Jan Peters and Joni Pajarinen
Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getting feedback via extrinsic rewards to train the agent, and in situations where this occurs very rarely the agent learns slowly or cannot learn at all. Simi...
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Xiaoping Zhang, Yuanpeng Zheng, Li Wang, Arsen Abdulali and Fumiya Iida
Multi-agent collaborative target search is one of the main challenges in the multi-agent field, and deep reinforcement learning (DRL) is a good way to learn such a task. However, DRL always faces the problem of sparse reward, which to some extent reduces...
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Sheng Yu, Wei Zhu and Yong Wang
Wargames are essential simulators for various war scenarios. However, the increasing pace of warfare has rendered traditional wargame decision-making methods inadequate. To address this challenge, wargame-assisted decision-making methods that leverage ar...
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Yu Chen, Qi Dong, Xiaozhou Shang, Zhenyu Wu and Jinyu Wang
Unmanned aerial vehicles (UAVs) are important in reconnaissance missions because of their flexibility and convenience. Vitally, UAVs are capable of autonomous navigation, which means they can be used to plan safe paths to target positions in dangerous su...
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Linfeng Su, Jinbo Wang and Hongbo Chen
The mission of hypersonic vehicles faces the problem of highly nonlinear dynamics and complex environments, which presents challenges to the intelligent level and real-time performance of onboard guidance algorithms. In this paper, inverse reinforcement ...
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Xiaoxiong Liu, Yi Yin, Yuzhan Su and Ruichen Ming
To solve the problems of autonomous decision making and the cooperative operation of multiple unmanned combat aerial vehicles (UCAVs) in beyond-visual-range air combat, this paper proposes an air combat decision-making method that is based on a multi-age...
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Jiabao Yu, Jiawei Chen, Ying Chen, Zhiguo Zhou and Junwei Duan
Although broad reinforcement learning (BRL) provides a more intelligent autonomous decision-making method for the collision avoidance problem of unmanned surface vehicles (USVs), the algorithm still has the problem of over-estimation and has difficulty c...
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Yubing Mao, Farong Gao, Qizhong Zhang and Zhangyi Yang
This study aims to solve the problem of sparse reward and local convergence when using a reinforcement learning algorithm as the controller of an AUV. Based on the generative adversarial imitation (GAIL) algorithm combined with a multi-agent, a multi-age...
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Xi Lyu, Yushan Sun, Lifeng Wang, Jiehui Tan and Liwen Zhang
This study aims to solve the problems of sparse reward, single policy, and poor environmental adaptability in the local motion planning task of autonomous underwater vehicles (AUVs). We propose a two-layer deep deterministic policy gradient algorithm-bas...
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Junfang Fan, Denghui Dou and Yi Ji
In this study, two different impact-angle-constrained guidance and control strategies using deep reinforcement learning (DRL) are proposed. The proposed strategies are based on the dual-loop and integrated guidance and control types. To address comprehen...
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Jiangyi Yao, Xiongwei Li, Yang Zhang, Jingyu Ji, Yanchao Wang, Danyang Zhang and Yicen Liu
Unmanned helicopter (UH) is often utilized for raid missions because it can evade radar detection by flying at ultra-low altitudes. Path planning is the key technology to realizing the autonomous action of UH. On the one hand, the dynamically changing ra...
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Junjie Zeng, Long Qin, Yue Hu, Quanjun Yin and Cong Hu
Since an individual approach can hardly navigate robots through complex environments, we present a novel two-level hierarchical framework called JPS-IA3C (Jump Point Search improved Asynchronous Advantage Actor-Critic) in this paper for robot navigation ...
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