Finally, through such a trialand-error process, the well-trained policy-based agent can be used to find evidential paths for predictions.
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.