Can pessimistic MBRL improve CAV multi-agent RL?
Multi-agent Uncertainty-Aware Pessimistic Model-Based Reinforcement Learning for Connected Autonomous Vehicles
March 27, 2025
https://arxiv.org/pdf/2503.20462This paper introduces MA-PMBRL, a new algorithm for training multiple AI agents (like self-driving cars) to make decisions in complex situations where they need to coordinate with each other but communication is limited. It uses a "pessimistic" approach, meaning the agents are trained to be cautious and assume the worst-case scenario to improve safety and reliability. The algorithm is designed to be efficient even with limited training data.
Key points for LLM-based multi-agent systems:
- Decentralized Training: Each agent learns independently with limited communication, relevant to distributed LLM agents.
- Partial Coverage: The algorithm acknowledges that training data might not cover every possible situation, an inherent characteristic of real-world LLM applications.
- Pessimistic Approach: Encourages robust behavior in uncertain situations, valuable for LLM agents deployed in unpredictable environments.
- Sample Efficiency: Optimized to learn effectively from limited data, addressing a key challenge in training large language models.
- Theoretical Guarantees: Provides a performance bound (PAC guarantee), which is important for understanding LLM-agent behavior and building trust.
- Communication Protocol: Introduces a strategy for efficient information exchange between agents under communication constraints, applicable to scenarios with restricted bandwidth for LLM agent interactions.