Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning
This work addresses urban safety perception and planning for experts, but it is incremental as it applies existing IRL methods to a new domain.
The authors tackled the problem of predicting urban safety by introducing an inverse reinforcement learning (IRL) framework to recover reward functions and model it as a Markov decision process, with results indicating promising prospects for IRL in this field.
Inspired by expert evaluation policy for urban perception, we proposed a novel inverse reinforcement learning (IRL) based framework for predicting urban safety and recovering the corresponding reward function. We also presented a scalable state representation method to model the prediction problem as a Markov decision process (MDP) and use reinforcement learning (RL) to solve the problem. Additionally, we built a dataset called SmallCity based on the crowdsourcing method to conduct the research. As far as we know, this is the first time the IRL approach has been introduced to the urban safety perception and planning field to help experts quantitatively analyze perceptual features. Our results showed that IRL has promising prospects in this field. We will later open-source the crowdsourcing data collection site and the model proposed in this paper.