通过状态占用正则化实现蒙特卡洛树搜索中可证明高效的长时程探索
Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization
AI总结：
针对蒙特卡洛树搜索长时程探索能力不足的问题，提出引入状态占用度量正则化的Volume-MCTS算法，可统一现有探索与规划方法，在机器人导航任务中性能优于AlphaZero，长时程探索效果显著提升。
摘要：
蒙特卡洛树搜索（MCTS）已在诸多领域取得成功，但与快速探索随机树等基于采样的运动规划算法相比，其在长时程探索方面仍面临挑战。为解决MCTS的这些局限，我们推导了一种基于策略优化、引入状态占用度量正则化的树搜索算法，将其命名为Volume-MCTS。我们证明，基于计数的探索方法和基于采样的运动规划方法均可作为该状态占用度量正则化目标的近似解推导得出。我们在多个机器人导航问题上测试了所提方法，发现Volume-MCTS性能优于AlphaZero，且展现出显著更优的长时程探索特性。
英文摘要：
Monte Carlo tree search (MCTS) has been successful in a variety of domains, but faces challenges with long-horizon exploration when compared to sampling-based motion planning algorithms like Rapidly-Exploring Random Trees. To address these limitations of MCTS, we derive a tree search algorithm based on policy optimization with state occupancy measure regularization, which we call {\it Volume-MCTS}. We show that count-based exploration and sampling-based motion planning can be derived as approximate solutions to this state occupancy measure regularized objective. We test our method on several robot navigation problems, and find that Volume-MCTS outperforms AlphaZero and displays significantly better long-horizon exploration properties.
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