ZYunfeii/UAV_Obstacle_Avoiding_DRL
Deep reinforcement learning project for autonomous UAV obstacle avoidance using multi-agent and single-agent RL algorithms.
Not currently ranked — collecting fresh signals.
star history
The project implements deep reinforcement learning algorithms for UAV autonomous navigation in both static and dynamic environments. In static environments it combines multi-agent reinforcement learning (MADDPG) with artificial potential field algorithms. In dynamic environments it applies PPO, TD3, DDPG, and SAC with disturbed flow field algorithms. It also includes traditional path planning methods (A*, RRT, ant colony) implemented in MATLAB and C++ for comparison.
Frequently asked
- What is ZYunfeii/UAV_Obstacle_Avoiding_DRL?
- Deep reinforcement learning project for autonomous UAV obstacle avoidance using multi-agent and single-agent RL algorithms.
- Is UAV_Obstacle_Avoiding_DRL open source?
- Yes — ZYunfeii/UAV_Obstacle_Avoiding_DRL is an open-source project tracked on heatdrop.
- What language is UAV_Obstacle_Avoiding_DRL written in?
- ZYunfeii/UAV_Obstacle_Avoiding_DRL is primarily written in Python.
- How popular is UAV_Obstacle_Avoiding_DRL?
- ZYunfeii/UAV_Obstacle_Avoiding_DRL has 724 stars on GitHub.
- Where can I find UAV_Obstacle_Avoiding_DRL?
- ZYunfeii/UAV_Obstacle_Avoiding_DRL is on GitHub at https://github.com/ZYunfeii/UAV_Obstacle_Avoiding_DRL.