mlsanityData to 7 Oct 2026

Rating-based Reinforcement Learning

arXiv 2307.16348 ↗First posted 30 Jul 2023cs.LG cs.AI cs.ROcited by 14

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Abstract (arXiv)

This paper develops a novel rating-based reinforcement learning approach that uses human ratings to obtain human guidance in reinforcement learning. Different from the existing preference-based and ranking-based reinforcement learning paradigms, based on human relative preferences over sample pairs, the proposed rating-based reinforcement learning approach is based on human evaluation of individual trajectories without relative comparisons between sample pairs. The rating-based reinforcement learning approach builds on a new prediction model for human ratings and a novel multi-class loss function. We conduct several experimental studies based on synthetic ratings and real human ratings to evaluate the effectiveness and benefits of the new rating-based reinforcement learning approach.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 22 of its 36 references that are in the map. First posted on arXiv 30 Jul 2023.

Builds on

22 of 36 references.

References · 22

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2017Deep reinforcement learning from human preferencescited by 3,846
  4. 2015Continuous control with deep reinforcement learningcited by 3,279
  5. 2016Concrete Problems in AI Safetycited by 1,582
  6. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400
  7. 2018Reward learning from human preferences and demonstrations in Ataricited by 314
  8. 2021PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-trainingcited by 298
  9. 2019Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observationscited by 256
  10. 2019Go-Explore: a New Approach for Hard-Exploration Problemscited by 207
  11. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 173
  12. 2017Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spacescited by 122
10 more
  1. 2021B-Pref: Benchmarking Preference-Based Reinforcement Learningcited by 106
  2. 2022SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learningcited by 96
  3. 2020Safe Imitation Learning via Fast Bayesian Reward Inference from Preferencescited by 73
  4. 2020Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferencescited by 71
  5. 2019Asking Easy Questions: A User-Friendly Approach to Active Reward Learningcited by 67
  6. 2020Preference-based Reinforcement Learning with Finite-Time Guaranteescited by 65
  7. 2022Reward Uncertainty for Exploration in Preference-based Reinforcement Learningcited by 60
  8. 2021APReL: A Library for Active Preference-based Reward Learning Algorithmscited by 14
  9. 2020Human-guided Robot Behavior Learning: A GAN-assisted Preference-based Reinforcement Learning Approachcited by 10
  10. 2023BC-IRL: Learning Generalizable Reward Functions from Demonstrationscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 3; 2026: 6.

14 papers.

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  1. 2024Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedbackcited by 20
  2. 2025Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Modelscited by 15
  3. 2024Listwise Reward Estimation for Offline Preference-based Reinforcement Learningcited by 14
  4. 2024Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learningcited by 6
  5. 2024Multi-Type Preference Learning: Empowering Preference-Based Reinforcement Learning with Equal Preferencescited by 2
  6. 2025Multi-Task Reward Learning from Human Ratingscited by 2
  7. 2025Human-Inspired Multi-Level Reinforcement Learningcited by 1
  8. 2026Learning the Value Systems of Agents with Preference-based and Inverse Reinforcement Learningcited by 1
  9. 2024Boosting Robustness in Preference-Based Reinforcement Learning with Dynamic Sparsitycited by 0
  10. 2026Reward Learning through Ranking Mean Squared Errorcited by 0
  11. 2026AirSplat: Alignment and Rating for Robust Feed-Forward 3D Gaussian Splattingcited by 0
  12. 2026Distributed Direct Preference Optimizationcited by 0
2 more
  1. 2026CoRe: Combined Rewards with Vision-Language Model Feedback for Preference-Aligned Reinforcement Learningcited by 0
  2. 2026Action- and Language-Conditioned Video Assessment for Embodied Controlcited by 0