基于自适应增强随机搜索的航天器追逃博弈策略研究
V448.2; 针对航天器与非合作目标追逃博弈的生存型微分对策拦截问题,基于强化学习研究了追逃博弈策略,提出了自适应增强随机搜索(adaptive-augmented random search,A-ARS)算法.针对序贯决策的稀疏奖励难题,设计了基于策略参数空间扰动的探索方法,加快策略收敛速度;针对可能过早陷入局部最优问题设计了新颖度函数并引导策略更新,可提升数据利用效率;通过数值仿真验证并与增强随机搜索(augmented random search,ARS)、近端策略优化算法(proximal policy optimization,PPO)以及深度确定性策略梯度下降算法(deep de...
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| Published in | 西北工业大学学报 Vol. 42; no. 1; pp. 117 - 128 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
航天飞行动力学技术国家级重点实验室,陕西 西安 710072%上海宇航系统工程研究所,上海 201108
01.02.2024
西北工业大学 航天学院,陕西 西安 710072 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1000-2758 |
| DOI | 10.1051/jnwpu/20244210117 |
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| Abstract | V448.2; 针对航天器与非合作目标追逃博弈的生存型微分对策拦截问题,基于强化学习研究了追逃博弈策略,提出了自适应增强随机搜索(adaptive-augmented random search,A-ARS)算法.针对序贯决策的稀疏奖励难题,设计了基于策略参数空间扰动的探索方法,加快策略收敛速度;针对可能过早陷入局部最优问题设计了新颖度函数并引导策略更新,可提升数据利用效率;通过数值仿真验证并与增强随机搜索(augmented random search,ARS)、近端策略优化算法(proximal policy optimization,PPO)以及深度确定性策略梯度下降算法(deep deterministic policy gradient,DDPG)进行对比,验证了此方法的有效性和先进性. |
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| AbstractList | V448.2; 针对航天器与非合作目标追逃博弈的生存型微分对策拦截问题,基于强化学习研究了追逃博弈策略,提出了自适应增强随机搜索(adaptive-augmented random search,A-ARS)算法.针对序贯决策的稀疏奖励难题,设计了基于策略参数空间扰动的探索方法,加快策略收敛速度;针对可能过早陷入局部最优问题设计了新颖度函数并引导策略更新,可提升数据利用效率;通过数值仿真验证并与增强随机搜索(augmented random search,ARS)、近端策略优化算法(proximal policy optimization,PPO)以及深度确定性策略梯度下降算法(deep deterministic policy gradient,DDPG)进行对比,验证了此方法的有效性和先进性. |
| Abstract_FL | To solve the problem of the survival differential policy interception between a spacecraft and a non-coop-erative target pursuit game,the pursuit game policy is studied based on reinforcement learning,and the adaptive-augmented random search algorithm is proposed.Firstly,to solve the sparse reward problem of sequential decision making,an exploration method based on the spatial perturbation of parameters of the policy is designed,thus accel-erating its convergence speed.Secondly,to avoid the possibility of falling into local optimum prematurely,a novelty degree function is designed to guide the policy update,enhancing the efficiency of data utilization.Finally,the ef-fectiveness and advancement of the exploration method are verified with numerical simulations and compared with those of the augmented random search algorithm,the proximal policy optimization algorithm and the deep determin-istic policy gradient algorithm. |
| Author | 焦杰 泮斌峰 苟永杰 吴文博 |
| AuthorAffiliation | 西北工业大学 航天学院,陕西 西安 710072;航天飞行动力学技术国家级重点实验室,陕西 西安 710072%上海宇航系统工程研究所,上海 201108 |
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| Author_FL | JIAO Jie GOU Yongjie WU Wenbo PAN Binfeng |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DOI | 10.1051/jnwpu/20244210117 |
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| DocumentTitle_FL | Research on game strategy of spacecraft chase and escape based on adaptive augmented random search |
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| Keywords | non-cooperative target 稀疏奖励 sparse reward pursuit game 微分对策 强化学习 differential game theory 非合作目标 追逃博弈 reinforcement learning |
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| Publisher | 航天飞行动力学技术国家级重点实验室,陕西 西安 710072%上海宇航系统工程研究所,上海 201108 西北工业大学 航天学院,陕西 西安 710072 |
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| Snippet | V448.2; 针对航天器与非合作目标追逃博弈的生存型微分对策拦截问题,基于强化学习研究了追逃博弈策略,提出了自适应增强随机搜索(adaptive-augmented random... |
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| Title | 基于自适应增强随机搜索的航天器追逃博弈策略研究 |
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