Multiple mini-robots navigation using a collaborative multiagent reinforcement learning framework

Chaysri, Piyabhum; Blekas, Konstantinos; Vlachos, Kostas

doi:10.1080/01691864.2020.1757507

Cited by 3 publications

(1 citation statement)

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“…An obstacle avoidance guidance algorithm was derived in [32] based on linear quadratic optimal control. An artificial potential field methodology was developed for path planning of cruise missile in [33]. However, these traditional obstacle avoidance algorithms are mostly applicable in stationary scenes.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical reinforcement learning guidance with threat avoidance

2022

J. of Syst. Eng. Electron.

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The guidance strategy is an extremely critical factor in determining the striking effect of the missile operation. A novel guidance law is presented by exploiting the deep reinforcement learning (DRL) with the hierarchical deep deterministic policy gradient (DDPG) algorithm. The reward functions are constructed to minimize the line-of-sight (LOS) angle rate and avoid the threat caused by the opposed obstacles. To attenuate the chattering of the acceleration, a hierarchical reinforcement learning structure and an improved reward function with action penalty are put forward. The simulation results validate that the missile under the proposed method can hit the target successfully and keep away from the threatened areas effectively.

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Section: Introductionmentioning

confidence: 99%