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In a Role-Playing Game, finding optimal trajectories is one of the mostimportant tasks. In fact, the strategy decision system becomes a key componentof a game engine. Determining the way in which decisions are taken (online,batch or simulated) and the consumed resources in decision making (e.g.execution time, memory) will influence, in mayor degree, the game performance.When classical search algorithms such as A* can be used, they are the veryfirst option. Nevertheless, such methods rely on precise and complete models ofthe search space, and there are many interesting scenarios where theirapplication is not possible. Then, model free methods for sequential decisionmaking under uncertainty are the best choice. In this paper, we propose aheuristic planning strategy to incorporate the ability of heuristic-search inpath-finding into a Dyna agent. The proposed Dyna-H algorithm, as A* does,selects branches more likely to produce outcomes than other branches. Besides,it has the advantages of being a model-free online reinforcement learningalgorithm. The proposal was evaluated against the one-step Q-Learning andDyna-Q algorithms obtaining excellent experimental results: Dyna-Hsignificantly overcomes both methods in all experiments. We suggest also, afunctional analogy between the proposed sampling from worst trajectoriesheuristic and the role of dreams (e.g. nightmares) in human behavior.

Dyna-H: a heuristic planning reinforcement learning algorithm applied to role-playing-game strategy decision systems