Recently, DeepPath (Xiong et al., 2017) and MINERVA (Das et al., 2018) were proposed to address the problem above by using reinforcement learning, where they are committed to learn a policy which guides the agent to find more superior evidential paths to maximize the expected reward.
deepPath parameterizes its policy with a fully-connected neural network and uses manual reward criteria, including global accuracy, efficiency and diversity, to evaluate the path quality.