Integral reinforcement learning solutions for a synchronisation system with constrained policies

Abouheaf, Mohammed I.; Mahmoud, Magdi S.; Gueaieb, Wail

doi:10.1049/iet-cta.2019.0397

Cited by 1 publication

(1 citation statement)

References 46 publications

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“…To help approximate the unknown value function and the associated optimal strategy, actor-critic neural networks are usually utilized [14]. The actor-critic structures have been adopted for cooperative control problems, such as graphical games and mobile sensor networks [28]- [30]. Despite their high potential in learning how to interact with ill-defined environments, RL schemes suffer from a major shortcoming stemming from the discrete (non-smooth) nature of their action space, which may lead to a "jerky" behavior when they are applied to control a real-world system.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Fuzzy Reinforcement Learning Cooperative Approach for the Autonomous Control of Flock Systems

Abouheaf

Gueaieb

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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The flock-guidance problem enjoys a challenging structure where multiple optimization objectives are solved simultaneously. This usually necessitates different control approaches to tackle various objectives, such as guidance, collision avoidance, and cohesion. The guidance schemes, in particular, have long suffered from complex tracking-error dynamics. Furthermore, techniques that are based on linear feedback strategies obtained at equilibrium conditions either may not hold or degrade when applied to uncertain dynamic environments. Pre-tuned fuzzy inference architectures lack robustness under such unmodeled conditions. This work introduces an adaptive distributed technique for the autonomous control of flock systems. Its relatively flexible structure is based on online fuzzy reinforcement learning schemes which simultaneously target a number of objectives; namely, following a leader, avoiding collision, and reaching a flock velocity consensus. In addition to its resilience in the face of dynamic disturbances, the algorithm does not require more than the agent position as a feedback signal. The effectiveness of the proposed method is validated with two simulation scenarios and benchmarked against a similar technique from the literature.

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Section: Introductionmentioning

confidence: 99%