# Autonomous Navigation via Deep Reinforcement Learning for Resource Constraint Edge Nodes using Transfer Learning

arXiv: https://arxiv.org/abs/1910.05547 · HTML: https://mlsanity.com/paper/1910.05547/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2019.

## Built on by (most cited first, 19 of 19)

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- [Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning](https://mlsanity.com/paper/2105.14780/) (2021)
- [Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review](https://mlsanity.com/paper/2208.12328/) (2022)
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- [DRLD-SP: A Deep Reinforcement Learning-based Dynamic Service Placement in Edge-Enabled Internet of Vehicles](https://mlsanity.com/paper/2106.06291/) (2021)
- [RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning](https://mlsanity.com/paper/2109.08231/) (2021)
- [NaviSplit: Dynamic Multi-Branch Split DNNs for Efficient Distributed Autonomous Navigation](https://mlsanity.com/paper/2406.13086/) (2024)
- [NaviSlim: Adaptive Context-Aware Navigation and Sensing via Dynamic Slimmable Networks](https://mlsanity.com/paper/2407.01563/) (2024)
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- [AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVs](https://mlsanity.com/paper/2102.02988/) (2021)
- [FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems](https://mlsanity.com/paper/2203.07276/) (2022)
- [Gesture Control of Micro-drone: A Lightweight-Net with Domain Randomization and Trajectory Generators](https://mlsanity.com/paper/2301.12470/) (2023)
- [Exploration in Knowledge Transfer Utilizing Reinforcement Learning](https://mlsanity.com/paper/2407.10835/) (2024)
- [Convex Distillation: Efficient Compression of Deep Networks via Convex Optimization](https://mlsanity.com/paper/2410.06567/) (2024)
- [Optimization of Infectious Disease Intervention Measures Based on Reinforcement Learning -- Empirical analysis based on UK COVID-19 epidemic data](https://mlsanity.com/paper/2505.04161/) (2025)
- [StarCraft+: Benchmarking Multi-agent Algorithms in Adversary Paradigm](https://mlsanity.com/paper/2512.16444/) (2025)

## Abstract (arXiv)

Smart and agile drones are fast becoming ubiquitous at the edge of the cloud. The usage of these drones are constrained by their limited power and compute capability. In this paper, we present a Transfer Learning (TL) based approach to reduce on-board computation required to train a deep neural network for autonomous navigation via Deep Reinforcement Learning for a target algorithmic performance. A library of 3D realistic meta-environments is manually designed using Unreal Gaming Engine and the network is trained end-to-end. These trained meta-weights are then used as initializers to the network in a test environment and fine-tuned for the last few fully connected layers. Variation in drone dynamics and environmental characteristics is carried out to show robustness of the approach. Using NVIDIA GPU profiler it was shown that the energy consumption and training latency is reduced by 3.7x and 1.8x respectively without significant degradation in the performance in terms of average distance traveled before crash i.e. Mean Safe Flight (MSF). The approach is also tested on a real environment using DJI Tello drone and similar results were reported.