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首页> 外文期刊>Knowledge-Based Systems >Dyna-H: A heuristic planning reinforcement learning algorithm applied to role-playing game strategy decision systems
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Dyna-H: A heuristic planning reinforcement learning algorithm applied to role-playing game strategy decision systems

机译：Dyna-H：一种启发式计划强化学习算法，应用于角色扮演游戏策略决策系统

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摘要

In a role-playing game, finding optimal trajectories is one of the most important tasks. In fact, the strategy decision system becomes a key component of a game engine. Determining the way in which decisions are taken (e.g. online, batch or simulated) and the consumed resources in decision making (e.g. execution time, memory) will influence, to a major degree, the game performance. When classical search algorithms such as A' can be used, they are the very first option. Nevertheless, such methods rely on precise and complete models of the search space so there are many interesting scenarios where its application is not possible, and hence, model free methods for sequential decision making under uncertainty are the best choice. In this paper, we propose a heuristic planning strategy to incorporate, into a Dyna agent, the ability of heuristic-search in path-finding. The proposed Dyna-H algorithm selects branches more likely to produce outcomes than other branches, just as A* does. However, unlike A", it has the advantages of a model-free online reinforcement learning algorithm. We evaluate our proposed algorithm against the one-step Q-learning and Dyna-Q algorithms and found that the Dyna-H, with its advantages, produced clearly superior results.
机译：在角色扮演游戏中，找到最佳轨迹是最重要的任务之一。实际上，战略决策系统已成为游戏引擎的关键组成部分。确定做出决策的方式（例如在线，批处理或模拟）以及决策中消耗的资源（例如执行时间，内存）将在很大程度上影响游戏性能。当可以使用经典搜索算法（例如A'）时，它们是第一个选择。然而，此类方法依赖于精确而完整的搜索空间模型，因此在许多有趣的场景中均无法应用，因此，在不确定性下进行顺序决策的无模型方法是最佳选择。在本文中，我们提出了一种启发式计划策略，以将Dyna代理中的启发式搜索功能纳入路径查找。与A *一样，拟议的Dyna-H算法选择的分支比其他分支更有可能产生结果。但是，与A“不同，它具有无模型在线增强学习算法的优势。我们针对单步Q学习和Dyna-Q算法评估了我们提出的算法，发现Dyna-H具有其优势，产生了明显优越的结果。

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