优化MCTS在随机冰面环境中提升决策效率
Optimized Monte Carlo Tree Search for Enhanced Decision Making in the FrozenLake Environment
- 结合累计奖励与访问次数，用UCT公式优化搜索策略
- 成功率和奖励均显著提升，收敛速度比基线快
- 适合研究随机环境下的强化学习决策方法
蒙特卡洛树搜索（MCTS）是一种强大的复杂决策算法。本文针对具有随机转移的典型强化学习任务FrozenLake，提出一种优化的MCTS实现。该方法利用累积奖励表和访问次数表，并结合上限置信区间树（UCT）公式，在滑溜网格世界中实现了高效学习。我们将其与基于策略和Q-learning的MCTS等其他决策算法进行对比，详细分析性能差异。结果表明，优化后的方案能有效最大化奖励与成功概率，同时显著缩短收敛时间，在存在固有随机性的环境中表现优于基线方法。
原文摘要 · Abstract (English)
Monte Carlo Tree Search (MCTS) is a powerful algorithm for solving complex decision-making problems. This paper presents an optimized MCTS implementation applied to the FrozenLake environment, a classic reinforcement learning task characterized by stochastic transitions. The optimization leverages cumulative reward and visit count tables along with the Upper Confidence Bound for Trees (UCT) formula, resulting in efficient learning in a slippery grid world. We benchmark our implementation against other decision-making algorithms, including MCTS with Policy and Q-Learning, and perform a detailed comparison of their performance. The results demonstrate that our optimized approach effectively maximizes rewards and success rates while minimizing convergence time, outperforming baseline methods, especially in environments with inherent randomness.
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