A biologically-inspired reinforcement learning based intelligent distributed flocking control for Multi-Agent Systems in presence of uncertain system and dynamic environment.
Mohammad JafariHao XuLuis Rodolfo García CarrilloPublished in: IFAC J. Syst. Control. (2020)
Keyphrases
- dynamic environments
- biologically inspired
- potential field
- multi agent systems
- agent technology
- agent systems
- reinforcement learning
- autonomous agents
- multi agent
- single agent
- adaptive control
- sensory motor
- mobile robot
- receptive fields
- reinforcement learning algorithms
- changing environment
- reinforcement learning agents
- multi agent reinforcement learning
- control system
- robot control
- multi agent environments
- path planning
- biologically plausible
- collision avoidance
- motor control
- learning rules
- biological systems
- spiking neural networks
- humanoid robot
- sensory information
- colour image segmentation
- control method
- learning capabilities
- model free
- control strategy
- multiagent systems
- control policy
- visual cortex
- collision free
- action selection
- markov decision processes
- optimal policy
- cooperative