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: CoRR (2020)
Keyphrases
- real time
- dynamic environments
- motion planning
- reinforcement learning
- multi agent
- path planning
- reinforcement learning agents
- mobile robot
- trajectory planning
- robotic tasks
- collision free
- single agent
- autonomous agents
- degrees of freedom
- potential field
- robot arm
- reinforcement learning algorithms
- multi robot
- obstacle avoidance
- collision avoidance
- multi agent environments
- humanoid robot
- belief space
- changing environment
- mechanical systems
- state space
- real robot
- unknown environments
- agent systems
- autonomous mobile robot
- robot control
- highly dynamic environments
- robotic arm
- partially observable
- sensory information
- configuration space
- partially observable markov decision processes
- path finding
- autonomous robots
- robotic systems
- markov decision processes
- multiagent systems
- intelligent agents
- viewpoint
- multi agent systems
- image sequences