Optimal game theoretic solution of the pursuit‐evasion intercept problem using on‐policy reinforcement learning

2021 ◽  
Author(s):  
Yusuf Kartal ◽  
Kamesh Subbarao ◽  
Atilla Dogan ◽  
Frank Lewis
Keyword(s):  
Reinforcement Learning ◽  
Pursuit Evasion ◽  
Game Theoretic ◽  
Theoretic Solution

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 A Game Theoretic Approach to Swarm Robotics

2006 ◽  
Vol 3 (3) ◽  
pp. 131-142 ◽  
Author(s):  
S. N. Givigi ◽  
H. M. Schwartz
Keyword(s):  
Game Theory ◽  
Reinforcement Learning ◽  
Personality Traits ◽  
Evolutionary Psychology ◽  
Learning Process ◽  
Swarm Robotics ◽  
Theoretic Approach ◽  
Intelligent Robots ◽  
Game Theoretic ◽  
Game Theoretic Approach

In this article, we discuss some techniques for achieving swarm intelligent robots through the use of traits of personality. Traits of personality are characteristics of each robot that, altogether, define the robot's behaviours. We discuss the use of evolutionary psychology to select a set of traits of personality that will evolve due to a learning process based on reinforcement learning. The use of Game Theory is introduced, and some simulations showing its potential are reported.

scholarly journals ERRATUM: "RATIONAL DYNAMICS AND EPISTEMIC LOGIC IN GAMES"

2007 ◽  
Vol 09 (02) ◽  
pp. 377-409 ◽  
Author(s):  
JOHAN VAN BENTHEM
Keyword(s):  
Backward Induction ◽  
Recursive Algorithms ◽  
Strategic Games ◽  
Solution Sets ◽  
Solution Algorithms ◽  
Mutual Knowledge ◽  
Dominated Strategies ◽  
Game Theoretic ◽  
Theoretic Solution ◽  
Extensive Games

Game-theoretic solution concepts describe sets of strategy profiles that are optimal for all players in some plausible sense. Such sets are often found by recursive algorithms like iterated removal of strictly dominated strategies in strategic games, or backward induction in extensive games. Standard logical analyses of solution sets use assumptions about players in fixed epistemic models for a given game, such as mutual knowledge of rationality. In this paper, we propose a different perspective, analyzing solution algorithms as processes of learning which change game models. Thus, strategic equilibrium gets linked to fixed-points of operations of repeated announcement of suitable epistemic statements. This dynamic stance provides a new look at the current interface of games, logic, and computation.

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