A Model-Free Multi-Agent Reinforcement Learning Approach to Reach a Robust, Optimal, and Environment-Friendly Power Management in a Micro-Grid.
M. Nasir UddinYazdan H. TabriziPublished in: IAS (2023)
Keyphrases
- model free
- multi agent reinforcement learning
- power management
- reinforcement learning
- power consumption
- average reward
- reinforcement learning algorithms
- function approximation
- distributed control
- mobile robot
- complex environments
- temporal difference
- policy iteration
- dynamic programming
- multi agent
- rl algorithms
- data center
- cooperative
- learning agents
- learning environment
- data mining
- stochastic games
- real robot
- optimal control
- autonomous agents
- multi agent systems
- support vector