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MARL describes learning and coordination methods for multiple autonomous agents in shared environments with cooperative or competitive objectives.
Multi-Agent Reinforcement Learning (MARL) studies learning and coordination among multiple autonomous agents sharing an environment. It addresses non-stationarity, scalability and coordination challenges via cooperative, competitive or mixed reward structures. MARL is applied in simulations, distributed control and multi-agent decision-making for complex dynamic systems.
Aggregated reward of all agents to measure collective performance.
Time until policies or performance stabilize.
Volume and frequency of messages exchanged between agents.
Multiple agents learn cooperative and competitive strategies for resource usage in a Gridworld.
Prototype implementation demonstrates distributed path planning and collision avoidance in real time.
Collection of standardized multi-agent environments for evaluating MARL algorithms.
Requirement analysis and selection of suitable scenarios
Set up simulation environment and agent interfaces
Select and implement MARL algorithms
Train, evaluate and iteratively adjust rewards