# Reinforcement Learning for UAV control with Policy and Reward Shaping

arXiv: https://arxiv.org/abs/2212.03828 · HTML: https://mlsanity.com/paper/2212.03828/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## Builds on (most cited first, 10 of 10)

- [FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback](https://mlsanity.com/paper/2001.06781/) (2020)
- [A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review](https://mlsanity.com/paper/2007.01544/) (2020)
- [Subgoal-based Reward Shaping to Improve Efficiency in Reinforcement Learning](https://mlsanity.com/paper/2104.06411/) (2021)
- [Guiding Reinforcement Learning Exploration Using Natural Language](https://mlsanity.com/paper/1707.08616/) (2017)
- [Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds](https://mlsanity.com/paper/1709.03969/) (2017)
- [A Comparison of Humanoid Robot Simulators: A Quantitative Approach](https://mlsanity.com/paper/2008.04627/) (2020)
- [Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning](https://mlsanity.com/paper/2009.09575/) (2020)
- [Persistent Rule-based Interactive Reinforcement Learning](https://mlsanity.com/paper/2102.02441/) (2021)
- [iCub: Learning Emotion Expressions using Human Reward](https://mlsanity.com/paper/2003.13483/) (2020)
- [Unmanned Aerial Vehicle Control Through Domain-based Automatic Speech Recognition](https://mlsanity.com/paper/2009.04215/) (2020)

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

- [AgenticRL: Agentic Reinforcement Learning with Self-Refinement for Complex UAV Navigation](https://mlsanity.com/paper/2606.03963/) (2026)

## Abstract (arXiv)

In recent years, unmanned aerial vehicle (UAV) related technology has expanded knowledge in the area, bringing to light new problems and challenges that require solutions. Furthermore, because the technology allows processes usually carried out by people to be automated, it is in great demand in industrial sectors. The automation of these vehicles has been addressed in the literature, applying different machine learning strategies. Reinforcement learning (RL) is an automation framework that is frequently used to train autonomous agents. RL is a machine learning paradigm wherein an agent interacts with an environment to solve a given task. However, learning autonomously can be time consuming, computationally expensive, and may not be practical in highly-complex scenarios. Interactive reinforcement learning allows an external trainer to provide advice to an agent while it is learning a task. In this study, we set out to teach an RL agent to control a drone using reward-shaping and policy-shaping techniques simultaneously. Two simulated scenarios were proposed for the training; one without obstacles and one with obstacles. We also studied the influence of each technique. The results show that an agent trained simultaneously with both techniques obtains a lower reward than an agent trained using only a policy-based approach. Nevertheless, the agent achieves lower execution times and less dispersion during training.