Interpretable UAV Collision Avoidance using Deep Reinforcement Learning
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Abstract (arXiv)
The significant components of any successful autonomous flight system are task completion and collision avoidance. Most deep learning algorithms successfully execute these aspects under the environment and conditions they are trained. However, they fail when subjected to novel environments. This paper presents an autonomous multi-rotor flight algorithm, using Deep Reinforcement Learning augmented with Self-Attention Models, that can effectively reason when subjected to varying inputs. In addition to their reasoning ability, they are also interpretable, enabling it to be used under real-world conditions. We have tested our algorithm under different weather conditions and environments and found it robust compared to conventional Deep Reinforcement Learning algorithms.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 37 references that are in the map. First posted on arXiv 25 May 2021.
Builds on
18 of 37 references.
References · 18
- 2017Attention Is All You Needcited by 60,361
- 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
- 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
- 2019Stand-Alone Self-Attention in Vision Modelscited by 542
- 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 523
- 2017A Brief Survey of Deep Reinforcement Learningcited by 483
- 2015Deep Recurrent Q-Learning for Partially Observable MDPscited by 465
- 2020Exploring Self-attention for Image Recognitioncited by 313
- 2019Recurrent Independent Mechanismscited by 219
- 2018An Introduction to Deep Reinforcement Learningcited by 206
- 2018Relational Deep Reinforcement Learningcited by 73
6 more
- 2020Neuroevolution of Self-Interpretable Agentscited by 49
- 2018Memory-based Deep Reinforcement Learning for Obstacle Avoidance in UAV with Limited Environment Knowledgecited by 26
- 2021A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidancecited by 10
- 2016Temporal Attention-Gated Model for Robust Sequence Classificationcited by 8
- 2019DQN with model-based exploration: efficient learning on environments with sparse rewardscited by 6
- 2019Efficient Object Detection Model for Real-Time UAV Applicationscited by 4
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1.
2 papers.