# Dual Agent Learning Based Aerial Trajectory Tracking

arXiv: https://arxiv.org/abs/2410.23571 · HTML: https://mlsanity.com/paper/2410.23571/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

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

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion](https://mlsanity.com/paper/2401.17583/) (2024)
- [AlphaPilot: Autonomous Drone Racing](https://mlsanity.com/paper/2005.12813/) (2020)
- [Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking](https://mlsanity.com/paper/2206.03305/) (2022)
- [Learning Vision-based Flight in Drone Swarms by Imitation](https://mlsanity.com/paper/1908.02999/) (2019)
- [Deep-PANTHER: Learning-Based Perception-Aware Trajectory Planner in Dynamic Environments](https://mlsanity.com/paper/2209.01268/) (2022)
- [A Meta-Learning-based Trajectory Tracking Framework for UAVs under Degraded Conditions](https://mlsanity.com/paper/2104.15081/) (2021)
- [Barrier Functions in Cascaded Controller: Safe Quadrotor Control](https://mlsanity.com/paper/1903.09711/) (2019)
- [Optimization-based Trajectory Tracking Approach for Multi-rotor Aerial Vehicles in Unknown Environments](https://mlsanity.com/paper/2202.06056/) (2022)

## Abstract (arXiv)

This paper presents a novel reinforcement learning framework for trajectory tracking of unmanned aerial vehicles in cluttered environments using a dual-agent architecture. Traditional optimization methods for trajectory tracking face significant computational challenges and lack robustness in dynamic environments. Our approach employs deep reinforcement learning (RL) to overcome these limitations, leveraging 3D pointcloud data to perceive the environment without relying on memory-intensive obstacle representations like occupancy grids. The proposed system features two RL agents: one for predicting UAV velocities to follow a reference trajectory and another for managing collision avoidance in the presence of obstacles. This architecture ensures real-time performance and adaptability to uncertainties. We demonstrate the efficacy of our approach through simulated and real-world experiments, highlighting improvements over state-of-the-art RL and optimization-based methods. Additionally, a curriculum learning paradigm is employed to scale the algorithms to more complex environments, ensuring robust trajectory tracking and obstacle avoidance in both static and dynamic scenarios.