How can MCTS improve CAV coordination?
A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles
September 24, 2024
https://arxiv.org/pdf/2409.13783This paper proposes a new Monte Carlo Tree Search (MCTS) algorithm for multi-vehicle cooperative driving, treating it as a multi-agent Markov game.
The key points for LLM-based multi-agent systems:
- Value-based MCTS: The algorithm uses a value function from reinforcement learning to guide action selection, similar to how LLMs can use value estimations for decision making.
- Parallel Update: A novel parallel update method is introduced, significantly improving search efficiency by leveraging similarities between actions in terms of safety. This has implications for managing large action spaces in LLM-based agents.
- Action Preference: The algorithm incorporates action preference based on potential reward, enabling more efficient exploration of promising actions. This relates to how LLMs can learn and prioritize actions based on predicted outcomes.