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It has been arduous to assess the progress of a policy learning algorithm inthe domain of hierarchical task with high dimensional action space due to thelack of a commonly accepted benchmark. In this work, we propose a newlight-weight benchmark task called Diner Dash for evaluating the performance ina complicated task with high dimensional action space. In contrast to thetraditional Atari games that only have a flat structure of goals and very fewactions, the proposed benchmark task has a hierarchical task structure and sizeof 57 for the action space and hence can facilitate the development of policylearning in complicated tasks. On top of that, we introduce Decomposed PolicyGraph Modelling (DPGM), an algorithm that combines both graph modelling anddeep learning to allow explicit domain knowledge embedding and achievessignificant improvement comparing to the baseline. In the experiments, we haveshown the effectiveness of the domain knowledge injection via a speciallydesigned imitation algorithm as well as results of other popular algorithms.

DinerDash Gym: A Benchmark for Policy Learning in High-Dimensional Action Space