论文标题
使用课程驱动的深入强化学习解决艰苦的AI计划实例
Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning
论文作者
论文摘要
尽管在一般的AI规划中取得了重大进展，但某些领域仍无法实现当前的AI规划系统。 Sokoban是一项PSPACE完整的计划任务，代表了当前AI计划者最困难的领域之一。由于硬实例上的指数搜索复杂性，即使是特定领域的专业搜索方法也很快失败。我们基于以课程驱动的方法来增强基于深入强化学习的方法是在培训的一天之内解决艰难实例的第一个方法，而其他现代求解器无法在任何合理的时间限制内解决这些实例。与使用精心手工修剪技术的先前努力相反，我们的方法自动发现了域结构。我们的结果表明，Deep RL提供了一个有希望的框架，以解决以前未解决的AI计划问题，只要可以设计适当的培训课程。
Despite significant progress in general AI planning, certain domains remain out of reach of current AI planning systems. Sokoban is a PSPACE-complete planning task and represents one of the hardest domains for current AI planners. Even domain-specific specialized search methods fail quickly due to the exponential search complexity on hard instances. Our approach based on deep reinforcement learning augmented with a curriculum-driven method is the first one to solve hard instances within one day of training while other modern solvers cannot solve these instances within any reasonable time limit. In contrast to prior efforts, which use carefully handcrafted pruning techniques, our approach automatically uncovers domain structure. Our results reveal that deep RL provides a promising framework for solving previously unsolved AI planning problems, provided a proper training curriculum can be devised.
