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CDT: Cascading Decision Trees for Explainable Reinforcement Learning
[article]
2021
arXiv
pre-print
Deep Reinforcement Learning (DRL) has recently achieved significant advances in various domains. However, explaining the policy of RL agents still remains an open problem due to several factors, one being the complexity of explaining neural networks decisions. Recently, a group of works have used decision-tree-based models to learn explainable policies. Soft decision trees (SDTs) and discretized differentiable decision trees (DDTs) have been demonstrated to achieve both good performance and
arXiv:2011.07553v2
fatcat:2kezrg5c4jbkzaxxotnngfqedu