# Synergistic Reinforcement and Imitation Learning for Vision-driven Autonomous Flight of UAV Along River

arXiv: https://arxiv.org/abs/2401.09332 · HTML: https://mlsanity.com/paper/2401.09332/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2001.11231/) (2020)
- [Flightmare: A Flexible Quadrotor Simulator](https://mlsanity.com/paper/2009.00563/) (2020)
- [Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving](https://mlsanity.com/paper/2103.10690/) (2021)
- [imitation: Clean Imitation Learning Implementations](https://mlsanity.com/paper/2211.11972/) (2022)
- [Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance](https://mlsanity.com/paper/1911.07109/) (2019)
- [Submodular Reinforcement Learning](https://mlsanity.com/paper/2307.13372/) (2023)

## Built on by (most cited first, 1 of 1)

- [PILOT: Privileged Imitation Learning for End-to-End Motion Planning of Autonomous UAVs under Partial Observability](https://mlsanity.com/paper/2608.14082/) (2026)

## Abstract (arXiv)

Vision-driven autonomous flight and obstacle avoidance of Unmanned Aerial Vehicles (UAVs) along complex riverine environments for tasks like rescue and surveillance requires a robust control policy, which is yet difficult to obtain due to the shortage of trainable riverine environment simulators. To easily verify the vision-based navigation controller performance for the river following task before real-world deployment, we developed a trainable photo-realistic dynamics-free riverine simulation environment using Unity. In this paper, we address the shortcomings that vanilla Reinforcement Learning (RL) algorithm encounters in learning a navigation policy within this partially observable, non-Markovian environment. We propose a synergistic approach that integrates RL and Imitation Learning (IL). Initially, an IL expert is trained on manually collected demonstrations, which then guides the RL policy training process. Concurrently, experiences generated by the RL agent are utilized to re-train the IL expert, enhancing its ability to generalize to unseen data. By leveraging the strengths of both RL and IL, this framework achieves a faster convergence rate and higher performance compared to pure RL, pure IL, and RL combined with static IL algorithms. The results validate the efficacy of the proposed method in terms of both task completion and efficiency. The code and trainable environments are available.