MAIDCRL: Semi-centralized Multi-agent Influence Dense-cnn Reinforcement Learning

Abstract

Distributed decision-making in multi-agent systems presents difficult challenges for interactive behavior learning in both cooperative and competitive systems. To mitigate this complexity, MAIDRL presents a semi-centralized Dense Reinforcement Learning algorithm enhanced by agent influence maps (AIMs), for learning effective multi-agent control on StarCraft Multi-Agent Challenge (SMAC) scenarios. In this paper, we extend the DenseNet in MAIDRL and introduce semi-centralized Multi-Agent Dense-CNN Reinforcement Learning, MAIDCRL, by incorporating convolutional layers into the deep model architecture, and evaluate the performance on both homogeneous and heterogeneous scenarios. The results show that the CNN-enabled MAIDCRL significantly improved the learning performance and achieved a faster learning rate compared to the existing MAIDRL, especially on more complicated heterogeneous SMAC scenarios. We further investigate the stability and robustness of our model. The statistics reflect tha

Stats

• citations4
• S2 citations—
• github stars0
• HF likes0
• heat score5.24
• arxiv keynipu2024maidcrl

Related papers

• Local Advantage Networks For Cooperative Multi-agent Reinforcement Learning (2021)0.00
• Deep Multi-agent Reinforcement Learning With Discrete-continuous Hybrid Action Spaces (2019)12.47
• D-HAL: Distributed Hierarchical Adversarial Learning For Multi-agent Interaction In Autonomous Intersection Management (2023)0.00
• Decentralized Multi-agent Reinforcement Learning With Networked Agents: Recent Advances (2019)0.00
• MANSA: Learning Fast And Slow In Multi-agent Systems (2023)0.00
• A Further Exploration Of Deep Multi-agent Reinforcement Learning With Hybrid Action Space (2022)5.84
• Deep Multi-agent Reinforcement Learning With Hybrid Action Spaces Based On Maximum Entropy (2022)0.00
• Cooperative Multi-agent Reinforcement Learning With Hypergraph Convolution (2021)5.84