# Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems

arXiv: https://arxiv.org/abs/2302.02921 · HTML: https://mlsanity.com/paper/2302.02921/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 32 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 17 of 17)

- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1805.01956/) (2018)
- [Learning Navigation Behaviors End-to-End with AutoRL](https://mlsanity.com/paper/1809.10124/) (2018)
- [PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning](https://mlsanity.com/paper/1710.03937/) (2017)
- [Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey](https://mlsanity.com/paper/2011.13112/) (2020)
- [Combining Optimal Control and Learning for Visual Navigation in Novel Environments](https://mlsanity.com/paper/1903.02531/) (2019)
- [Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians](https://mlsanity.com/paper/2102.13073/) (2021)
- [NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments](https://mlsanity.com/paper/2012.04406/) (2020)
- [Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems](https://mlsanity.com/paper/2104.03616/) (2021)
- [All-in-One: A DRL-based Control Switch Combining State-of-the-art Navigation Planners](https://mlsanity.com/paper/2109.11636/) (2021)
- [Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators](https://mlsanity.com/paper/2104.03663/) (2021)
- [Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles](https://mlsanity.com/paper/2008.12451/) (2020)
- [Online Motion Planning based on Nonlinear Model Predictive Control with Non-Euclidean Rotation Groups](https://mlsanity.com/paper/2006.03534/) (2020)
- [Deep-Reinforcement-Learning-Based Semantic Navigation of Mobile Robots in Dynamic Environments](https://mlsanity.com/paper/2008.00516/) (2020)
- [Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree Search](https://mlsanity.com/paper/2202.06443/) (2022)
- [Obstacle-aware Waypoint Generation for Long-range Guidance of Deep-Reinforcement-Learning-based Navigation Approaches](https://mlsanity.com/paper/2109.11639/) (2021)

## Built on by (most cited first, 2 of 2)

- [Evolutionary Curriculum Training for DRL-Based Navigation Systems](https://mlsanity.com/paper/2306.08870/) (2023)
- [Avoidance Navigation Based on Offline Pre-Training Reinforcement Learning](https://mlsanity.com/paper/2308.01551/) (2023)

## Abstract (arXiv)

In recent years, Deep Reinforcement Learning emerged as a promising approach for autonomous navigation of ground vehicles and has been utilized in various areas of navigation such as cruise control, lane changing, or obstacle avoidance. However, most research works either focus on providing an end-to-end solution training the whole system using Deep Reinforcement Learning or focus on one specific aspect such as local motion planning. This however, comes along with a number of problems such as catastrophic forgetfulness, inefficient navigation behavior, and non-optimal synchronization between different entities of the navigation stack. In this paper, we propose a holistic Deep Reinforcement Learning training approach in which the training procedure is involving all entities of the navigation stack. This should enhance the synchronization between- and understanding of all entities of the navigation stack and as a result, improve navigational performance. We trained several agents with a number of different observation spaces to study the impact of different input on the navigation behavior of the agent. In profound evaluations against multiple learning-based and classic model-based navigation approaches, our proposed agent could outperform the baselines in terms of efficiency and safety attaining shorter path lengths, less roundabout paths, and less collisions.