Meta-MARL：Multi-Agent Reinforcement and Meta-Learning

The meta-MARL algorithm combines the COMA multi-agent reinforcement learning algorithm based on the actor-critic framework with the MAML meta-learning algorithm, and applies it to the StarCraft game scene.

Model Architecture Diagram：

Description： To address the issue of reduced performance of multi-agent reinforcement learning models in complex and dynamic game environments, the meta-MARL algorithm combines the COMA multi-agent reinforcement learning algorithm based on the actor-critic framework with the MAML meta-learning algorithm and applies it to the StarCraft game scene. The algorithm leverages the idea of meta-learning to help it adapt quickly to new scenarios and run normally in a constantly changing and complex environment.

Source Code