scholarly journals Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning

2022 ◽  
Vol 12 (2) ◽  
pp. 610
Author(s):  
Ralvi Isufaj ◽  
Marsel Omeri ◽  
Miquel Angel Piera
Keyword(s):  
Conflict Resolution ◽  
Reinforcement Learning ◽  
Unmanned Aircraft ◽  
Traffic Density ◽  
Primary Concern ◽  
Learning Problem ◽  
Cooperative Agents ◽  
Graph Neural Networks ◽  
Expected Increase ◽  
Multi Uav

Safety is the primary concern when it comes to air traffic. In-flight safety between Unmanned Aircraft Vehicles (UAVs) is ensured through pairwise separation minima, utilizing conflict detection and resolution methods. Existing methods mainly deal with pairwise conflicts, however, due to an expected increase in traffic density, encounters with more than two UAVs are likely to happen. In this paper, we model multi-UAV conflict resolution as a multiagent reinforcement learning problem. We implement an algorithm based on graph neural networks where cooperative agents can communicate to jointly generate resolution maneuvers. The model is evaluated in scenarios with 3 and 4 present agents. Results show that agents are able to successfully solve the multi-UAV conflicts through a cooperative strategy.

scholarly journals Cooperative Multi-UAV Collision Avoidance Based on a Complex Network

2019 ◽  
Vol 9 (19) ◽  
pp. 3943 ◽  
Author(s):  
Huang ◽  
Tang ◽  
Lao
Keyword(s):  
Conflict Resolution ◽  
Collision Avoidance ◽  
Complex Network ◽  
Computing Time ◽  
Real Life ◽  
Traffic Density ◽  
Contraction Method ◽  
Cn Algorithm ◽  
Multi Uav ◽  
Key Nodes

The conflict resolution problem in cooperative unmanned aerial vehicle (UAV) clusters sharing a three-dimensional airspace with increasing air traffic density is very important. This paper innovatively solves this problem by employing the complex network (CN) algorithm. The proposed approach allows a UAV to perform only one maneuver—that of the flight level change. The novel UAV conflict resolution is divided into two steps, corresponding to the key node selection (KS) algorithm based on the node contraction method and the sense selection (SS) algorithm based on an objective function. The efficiency of the cooperative multi-UAV collision avoidance (CA) system improved a lot due to the simple two-step collision avoidance logic. The paper compares the difference between random selection and the use of the node contraction method to select key nodes. Experiments showed that using the node contraction method to select key nodes can make the collision avoidance effect of UAVs better. The CA maneuver was validated with quantitative simulation experiments, demonstrating advantages such as minimal cost when considering the robustness of the global traffic situation, as well as significant real-time and high efficiency. The CN algorithm requires a relatively small computing time that renders the approach highly suitable for solving real-life operational situations.

QoE-driven Adaptive Deployment Strategy of Multi-UAV Networks Based on Hybrid Deep Reinforcement Learning

2021 ◽  
pp. 1-1
Author(s):  
Yi Zhou ◽  
Xiaoyong Ma ◽  
Shuting Hu ◽  
Danyang Zhou ◽  
Nan Cheng ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Deployment Strategy ◽  
Multi Uav

Collaborative Computation Offloading and Resource Allocation in Multi-UAV Assisted IoT Networks: A Deep Reinforcement Learning Approach

2021 ◽  
pp. 1-1
Author(s):  
Abegaz Mohammed Seid ◽  
Gordon Owusu Boateng ◽  
Stephen Anokye ◽  
Thomas Kwantwi ◽  
Guolin Sun ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Computation Offloading ◽  
Learning Approach ◽  
Multi Uav

scholarly journals A Gradient-Based Reinforcement Learning Algorithm for Multiple Cooperative Agents

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 70223-70235 ◽  
Author(s):  
Zhen Zhang ◽  
Dongqing Wang ◽  
Dongbin Zhao ◽  
Qiaoni Han ◽  
Tingting Song
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Cooperative Agents ◽  
Gradient Based ◽  
Reinforcement Learning Algorithm

scholarly journals Temporal Uncertainty During Overshadowing

2011 ◽  
pp. 46-55
Author(s):  
Dómhnall J. Jennings ◽  
Eduardo Alonso ◽  
Esther Mondragón ◽  
Charlotte Bonardi
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Associative Learning ◽  
Learning Theories ◽  
Temporal Difference ◽  
Temporal Uncertainty ◽  
Learning Problem ◽  
Difference Model ◽  
Distribution Form ◽  
Temporal Properties

Standard associative learning theories typically fail to conceptualise the temporal properties of a stimulus, and hence cannot easily make predictions about the effects such properties might have on the magnitude of conditioning phenomena. Despite this, in intuitive terms we might expect that the temporal properties of a stimulus that is paired with some outcome to be important. In particular, there is no previous research addressing the way that fixed or variable duration stimuli can affect overshadowing. In this chapter we report results which show that the degree of overshadowing depends on the distribution form - fixed or variable - of the overshadowing stimulus, and argue that conditioning is weaker under conditions of temporal uncertainty. These results are discussed in terms of models of conditioning and timing. We conclude that the temporal difference model, which has been extensively applied to the reinforcement learning problem in machine learning, accounts for the key findings of our study.

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