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MA-VLCM: A Vision Language Critic Model for Value Estimation of Policies in Multi-Agent Team Settings
🤖AI Summary
Researchers propose MA-VLCM, a framework that uses pretrained vision-language models as centralized critics in multi-agent reinforcement learning instead of learning critics from scratch. This approach significantly improves sample efficiency and enables zero-shot generalization while producing compact policies suitable for resource-constrained robots.
Key Takeaways
- →MA-VLCM replaces learned centralized critics in multi-agent reinforcement learning with fine-tuned pretrained vision-language models.
- →The framework eliminates the need for critic learning during policy optimization, significantly improving sample efficiency.
- →The approach produces compact execution policies suitable for deployment on resource-constrained robots.
- →Results demonstrate good zero-shot return estimation across different VLM backbones in both in-distribution and out-of-distribution scenarios.
- →The system addresses computational constraints in heterogeneous multi-robot systems with diverse embodiments.
#multi-agent-reinforcement-learning#vision-language-models#robotics#sample-efficiency#zero-shot-learning#policy-optimization#vlm#marl
Read Original →via arXiv – CS AI
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