论文标题
通过最大的熵深度逆增强学习来评估城市城市的感知安全性
Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning
论文作者
论文摘要
受到城市感知的专家评估政策的启发，我们提出了一种基于新型的逆强化学习（IRL）框架来预测城市安全并恢复相应的奖励功能。我们还提出了一种可扩展的状态表示方法，将预测问题建模为马尔可夫决策过程（MDP），并使用加固学习（RL）来解决问题。此外，我们基于众包方法来构建了一个名为SmallCity的数据集，以进行研究。据我们所知，这是IRL方法第一次引入城市安全感知和计划领域，以帮助专家定量分析感知特征。我们的结果表明，IRL在该领域具有有希望的前景。稍后，我们将开源众包数据收集网站和本文提出的模型。
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.
