优化的蒙特卡洛树搜索用于增强FrozenLake环境中的决策制定
Optimized Monte Carlo Tree Search for Enhanced Decision Making in the FrozenLake Environment
AI总结：
本文提出一种优化的蒙特卡洛树搜索方法，利用累积奖励、访问计数表和UCT公式应用于FrozenLake环境，有效提高了奖励和成功率并缩短收敛时间，优于带策略的MCTS和Q-Learning等基线算法。
摘要：
蒙特卡洛树搜索（MCTS）是解决复杂决策问题的强大算法。本文提出了一种应用于FrozenLake环境的优化MCTS实现，该环境是一个具有随机转移特征的经典强化学习任务。该优化利用累积奖励和访问计数表以及树的上置信界（UCT）公式，从而在湿滑网格世界中实现了高效学习。我们将该实现与其他决策算法（包括带策略的MCTS和Q-Learning）进行了基准测试，并对其性能进行了详细比较。结果表明，我们优化的方法有效地最大化了奖励和成功率，同时最小化了收敛时间，优于基线方法，尤其是在具有内在随机性的环境中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
