mlsanityData to 13 Oct 2026

Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning

arXiv 2211.10660 ↗First posted 19 Nov 2022cs.LG cs.AIcited by 0

On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data. Builds on 17 of its 45 references that are in the map. First posted on arXiv 19 Nov 2022.

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17 of 45 references.

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  1. 2019MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning17 Dec

Other references · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,950
  2. 2017Proximal Policy Optimization Algorithmscited by 14,592
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,656
  4. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,216
  5. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,930
  6. 2016Pyramid Scene Parsing Networkcited by 2,474
  7. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,710
  8. 2014Deep Visual-Semantic Alignments for Generating Image Descriptionscited by 967
  9. 2021Segmenter: Transformer for Semantic Segmentationcited by 855
  10. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 772
  11. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 294
  12. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 175
4 more
  1. 2018On Learning Intrinsic Rewards for Policy Gradient Methodscited by 101
  2. 2016Deep Learning the City : Quantifying Urban Perception At A Global Scalecited by 65
  3. 2020Predicting Goal-directed Human Attention Using Inverse Reinforcement Learningcited by 53
  4. 2021Generative Adversarial Reward Learning for Generalized Behavior Tendency Inferencecited by 6

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  2. 2025TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning
  3. 2022Driving in Real Life with Inverse Reinforcement Learning
  4. 2024Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds
  5. 2025Anomalous Decision Discovery using Inverse Reinforcement Learning
  6. 2022Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving
  7. 2024Discovering Cyclists' Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement Learning
  8. 2019Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving