Multi-Agent Reinforcement Learning-Based UAV Pathfinding for Obstacle Avoidance in Stochastic Environment

2023-10-25 · Qizhen Wu, Kexin Liu, Lei Chen, Jinhu Lü

Traditional methods plan feasible paths for multiple agents in the stochastic environment. However, the methods' iterations with the changes in the environment result in computation complexities, especially for the decentralized agents without a centralized planner. Although reinforcement learning provides a plausible solution because of the generalization for different environments, it struggles with enormous agent-environment interactions in training. Here, we propose a novel centralized training with decentralized execution method based on multi-agent reinforcement learning, which is improved based on the idea of model predictive control. In our approach, agents communicate only with the centralized planner to make decentralized decisions online in the stochastic environment. Furthermore, considering the communication constraint with the centralized planner, each agent plans feasible paths through the extended observation, which combines information on neighboring agents based on the distance-weighted mean field approach. Inspired by the rolling optimization approach of model predictive control, we conduct multi-step value convergence in multi-agent reinforcement learning to enhance the training efficiency, which reduces the expensive interactions in convergence. Experiment results in both comparison, ablation, and real-robot studies validate the effectiveness and generalization performance of our method.

📄 PDF Abstract BibTeX arXiv:2310.16659

Code (1)

wu-duanduan/ctfde_mpc 공식 구현 pytorch

Tasks

Model Predictive ControlMulti-agent Reinforcement Learning reinforcement-learning Reinforcement Learning

Multi-agent navigation based on deep reinforcement learning and traditional pathfinding algorithm

2020-12-05 · Hongda Qiu

We develop a new framework for multi-agent collision avoidance problem. The framework combined traditional pathfinding algorithm and reinforcement learning. In our approach, the agents learn whether to be navigated or to…

Collision Avoidance Deep Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning

2021-11-14 · Yuzi Yan, Xiaoxiang Li, Xinyou Qiu, Jiantao Qiu 외

Multi-agent formation as well as obstacle avoidance is one of the most actively studied topics in the field of multi-agent systems. Although some classic controllers like model predictive control (MPC) and fuzzy control …

Model Predictive Control Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Improved Obstacle Avoidance for Autonomous Robots with ORCA-FLC

2025-08-08 · Justin London arxiv

Obstacle avoidance enables autonomous agents and robots to operate safely and efficiently in dynamic and complex environments, reducing the risk of collisions and damage. For a robot or autonomous system to successfully …

Reinforcement Learning Collision Avoidance

Virtual Experience to Real World Application: Sidewalk Obstacle Avoidance Using Reinforcement Learning for Visually Impaired

2020-09-27 · Faruk Ahmed, Md Sultan Mahmud, Kazi Ashraf Moinuddin, Mohammed Istiaque Hyder 외

Finding a path free from obstacles that poses minimal risk is critical for safe navigation. People who are sighted and people who are visually impaired require navigation safety while walking on a sidewalk. In this resea…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Dynamic Interval Restrictions on Action Spaces in Deep Reinforcement Learning for Obstacle Avoidance

2023-06-13 · Tim Grams

Deep reinforcement learning algorithms typically act on the same set of actions. However, this is not sufficient for a wide range of real-world applications where different subsets are available at each step. In this the…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning