Multi-Agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning.
Samaneh Hosseini SemnaniHugh LiuMichael EverettAnton H. J. de RuiterJonathan P. HowPublished in: IEEE Robotics Autom. Lett. (2020)
Keyphrases
- dynamic environments
- motion planning
- reinforcement learning
- multi agent
- path planning
- reinforcement learning agents
- mobile robot
- trajectory planning
- single agent
- robotic tasks
- collision free
- potential field
- autonomous agents
- robot arm
- degrees of freedom
- multi robot
- obstacle avoidance
- collision avoidance
- reinforcement learning algorithms
- belief space
- multi agent systems
- autonomous mobile robot
- multi agent environments
- agent systems
- model free
- configuration space
- state space
- robotic arm
- intelligent agents
- robot control
- humanoid robot
- partially observable
- machine learning
- learning algorithm
- unknown environments
- viewpoint
- dynamic programming
- path finding
- mechanical systems
- climbing robot
- real time
- action selection
- multiple agents
- changing environment
- robotic systems
- optimal policy