arXiv:2307.01472v2 Announce Type: replace-cross Abstract: We present a novel Diffusion Offline Multi-agent Model (DOM2) for offline Multi-Agent Reinforcement Learning (MARL). Different from existing algorithms that rely mainly on conservatism in policy design, DOM2 enhances policy expressiveness and diversity based on diffusion model. Specifically, we incorporate a diffusion model into the policy network and propose a trajectory-based data-reweighting scheme in training. These key ingredients significantly improve algorithm robustness against environment changes and achieve significant improve
The proliferation of complex multi-agent systems and the need for robust learning in data-constrained offline settings are driving innovation in AI, leveraging recent advances in diffusion models.
This research addresses fundamental limitations in multi-agent reinforcement learning, potentially enabling more generalizable and data-efficient AI agents crucial for complex real-world applications.
Traditional policy design in offline multi-agent RL is being replaced by more expressive diffusion-based models, enhancing robustness against environmental changes and improving learning from limited data.
- · AI developers
- · Robotics industry
- · Logistics and supply chain automation
- · Deep learning research community
- · Organizations relying on rigid, less adaptable AI systems
- · Traditional reinforcement learning algorithms in complex offline settings
Improved performance and broader applicability of multi-agent AI systems in real-world scenarios due to enhanced generalization and data efficiency.
Reduced data requirements for training complex AI systems could accelerate deployment across various industries, creating new autonomous capabilities.
The integration of diffusion models could become a standard component in agentic system architectures, fostering a new generation of more robust and adaptable AI agents.
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