scholarly journals Two-Stage Pursuit Strategy for Incomplete-Information Impulsive Space Pursuit-Evasion Mission Using Reinforcement Learning

Aerospace ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 299
Author(s):  
Bin Yang ◽  
Pengxuan Liu ◽  
Jinglang Feng ◽  
Shuang Li
Keyword(s):  
Reinforcement Learning ◽  
Incomplete Information ◽  
Trajectory Optimization ◽  
Game Problem ◽  
Gradient Algorithm ◽  
Two Stage ◽  
Pursuit Evasion ◽  
Evasion Game ◽  
Close Distance ◽  
Pursuit Strategy

This paper presents a novel and robust two-stage pursuit strategy for the incomplete-information impulsive space pursuit-evasion missions considering the J2 perturbation. The strategy firstly models the impulsive pursuit-evasion game problem into a far-distance rendezvous stage and a close-distance game stage according to the perception range of the evader. For the far-distance rendezvous stage, it is transformed into a rendezvous trajectory optimization problem and a new objective function is proposed to obtain the pursuit trajectory with the optimal terminal pursuit capability. For the close-distance game stage, a closed-loop pursuit approach is proposed using one of the reinforcement learning algorithms, i.e., the deep deterministic policy gradient algorithm, to solve and update the pursuit trajectory for the incomplete-information impulsive pursuit-evasion missions. The feasibility of this novel strategy and its robustness to different initial states of the pursuer and evader and to the evasion strategies are demonstrated for the sun-synchronous orbit pursuit-evasion game scenarios. The results of the Monte Carlo tests show that the successful pursuit ratio of the proposed method is over 91% for all the given scenarios.

scholarly journals An Improved Approach towards Multi-Agent Pursuit–Evasion Game Decision-Making Using Deep Reinforcement Learning

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1433
Author(s):  
Kaifang Wan ◽  
Dingwei Wu ◽  
Yiwei Zhai ◽  
Bo Li ◽  
Xiaoguang Gao ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Superior Performance ◽  
State Variables ◽  
Multi Agent Systems ◽  
Adversarial Learning ◽  
Pursuit Evasion ◽  
Evasion Game ◽  
Multi Agent ◽  
Adversarial Attack

A pursuit–evasion game is a classical maneuver confrontation problem in the multi-agent systems (MASs) domain. An online decision technique based on deep reinforcement learning (DRL) was developed in this paper to address the problem of environment sensing and decision-making in pursuit–evasion games. A control-oriented framework developed from the DRL-based multi-agent deep deterministic policy gradient (MADDPG) algorithm was built to implement multi-agent cooperative decision-making to overcome the limitation of the tedious state variables required for the traditionally complicated modeling process. To address the effects of errors between a model and a real scenario, this paper introduces adversarial disturbances. It also proposes a novel adversarial attack trick and adversarial learning MADDPG (A2-MADDPG) algorithm. By introducing an adversarial attack trick for the agents themselves, uncertainties of the real world are modeled, thereby optimizing robust training. During the training process, adversarial learning was incorporated into our algorithm to preprocess the actions of multiple agents, which enabled them to properly respond to uncertain dynamic changes in MASs. Experimental results verified that the proposed approach provides superior performance and effectiveness for pursuers and evaders, and both can learn the corresponding confrontational strategy during training.

Modeling and Solving of the Missile Pursuit-Evasion Game Problem

2021 ◽  
Author(s):  
Dingding Qi ◽  
Longyue Li ◽  
Hailong Xu ◽  
Ye Tian ◽  
Huizhen Zhao
Keyword(s):  
Game Problem ◽  
Pursuit Evasion ◽  
Evasion Game

scholarly journals A Simplified Pursuit-evasion Game with Reinforcement Learning

2021 ◽  
Vol 65 (2) ◽  
pp. 160-166
Author(s):  
Gabor Paczolay ◽  
Istvan Harmati
Keyword(s):  
Linear Programming ◽  
Reinforcement Learning ◽  
Programming Problem ◽  
Collision Avoidance ◽  
Linear Programming Problem ◽  
The State ◽  
State Information ◽  
Pursuit Evasion ◽  
Pursuit And Evasion ◽  
Evasion Game

In this paper we visit the problem of pursuit and evasion and specifically, the collision avoidance during the problem. Two distinct tasks are visited: the first is a scenario when the agents can communicate with each other online, meanwhile in the second scenario they have to only rely on the state information and the knowledge about other agents' actions. We propose a method combining the already existing Minimax-Q and Nash-Q algorithms to provide a solution that can better take the enemy as well as friendly agents' actions into consideration. This combination is a simple weighting of the two algorithms with the Minimax-Q algorithm being based on a linear programming problem.

Intercepting a Superior Missile: Trajectory Optimization Approach to a Pursuit-Evasion Game

2020 ◽  
pp. 2050004
Author(s):  
Mohammad Altaher ◽  
Omaima Nomir ◽  
Samir ElMougy
Keyword(s):  
Optimal Control ◽  
Trajectory Optimization ◽  
Reduction Technique ◽  
Differential Flatness ◽  
Optimization Approach ◽  
Control Techniques ◽  
Pursuit Evasion ◽  
Evasion Game ◽  
Dimensionality Reduction Technique ◽  
Path Constraint

This paper handles the problem of intercepting a superior missile using a formation of lower-capabilities pursuers. The problem is formulated as multi-player differential game and solved using optimal control techniques. This approach makes use of the Apollonius circles as a final path constraint of the problem in order to achieve the capturing conditions. Additionally, a dimensionality reduction technique based on differential flatness property of the formation is investigated.

Sign in / Sign up

Export Citation Format

Share Document