# Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk

arXiv: https://arxiv.org/abs/2212.04123 · HTML: https://mlsanity.com/paper/2212.04123/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 71 references that are in the map.
- First posted on arXiv 8 Dec 2022.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Quantifying Generalization in Reinforcement Learning](https://mlsanity.com/paper/1812.02341/) (2018)
- [Multi-task Deep Reinforcement Learning with PopArt](https://mlsanity.com/paper/1809.04474/) (2018)
- [Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1805.01956/) (2018)
- [Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning](https://mlsanity.com/paper/1709.10082/) (2017)
- [A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving](https://mlsanity.com/paper/1909.07707/) (2019)
- [Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems](https://mlsanity.com/paper/2104.03616/) (2021)
- [Zipfian environments for Reinforcement Learning](https://mlsanity.com/paper/2203.08222/) (2022)
- [Hierarchical Reinforcement Learning Framework towards Multi-agent Navigation](https://mlsanity.com/paper/1807.05424/) (2018)

## Built on by (most cited first, 5 of 5)

- [Robust Path Following on Rivers Using Bootstrapped Reinforcement Learning](https://mlsanity.com/paper/2303.15178/) (2023)
- [2-Level Reinforcement Learning for Ships on Inland Waterways: Path Planning and Following](https://mlsanity.com/paper/2307.16769/) (2023)
- [Self-organized free-flight arrival for urban air mobility](https://mlsanity.com/paper/2404.03710/) (2024)
- [Two-step dynamic obstacle avoidance](https://mlsanity.com/paper/2311.16841/) (2023)
- [Real-Time Adaptive Motion Planning via Point Cloud-Guided, Energy-Based Diffusion and Potential Fields](https://mlsanity.com/paper/2507.09383/) (2025)

## Abstract (arXiv)

In the field of autonomous robots, reinforcement learning (RL) is an increasingly used method to solve the task of dynamic obstacle avoidance for mobile robots, autonomous ships, and drones. A common practice to train those agents is to use a training environment with random initialization of agent and obstacles. Such approaches might suffer from a low coverage of high-risk scenarios in training, leading to impaired final performance of obstacle avoidance. This paper proposes a general training environment where we gain control over the difficulty of the obstacle avoidance task by using short training episodes and assessing the difficulty by two metrics: The number of obstacles and a collision risk metric. We found that shifting the training towards a greater task difficulty can massively increase the final performance. A baseline agent, using a traditional training environment based on random initialization of agent and obstacles and longer training episodes, leads to a significantly weaker performance. To prove the generalizability of the proposed approach, we designed two realistic use cases: A mobile robot and a maritime ship under the threat of approaching obstacles. In both applications, the previous results can be confirmed, which emphasizes the general usability of the proposed approach, detached from a specific application context and independent of the agent's dynamics. We further added Gaussian noise to the sensor signals, resulting in only a marginal degradation of performance and thus indicating solid robustness of the trained agent.