Can MCTS improve Uno AI with better rewards?
Improve Value Estimation of Q Function and Reshape Reward with Monte Carlo Tree Search
October 16, 2024
https://arxiv.org/pdf/2410.11642This research introduces a novel algorithm combining Double Deep Q-Learning with Monte Carlo Tree Search (DDQN with MCTS) to improve gameplay in Uno, an imperfect information game.
The key takeaways for LLM-based multi-agent systems are:
- Addressing reward sparsity: The algorithm tackles the challenge of sparse rewards, a common issue in multi-agent systems, by reshaping the reward structure based on MCTS simulations, leading to more frequent positive feedback during training.
- Improved Q-value estimation: Using MCTS to evaluate states and guide action selection leads to more accurate Q-value estimates compared to traditional DDQN, ultimately resulting in better policy learning.
- Applicability beyond Uno: The core concepts of integrating MCTS with reinforcement learning techniques like DDQN can be generalized and applied to other multi-agent scenarios, including those using LLMs.