通用机器学习 · reinforcement learning论文趋势 2023–2026

通用机器学习：reinforcement_learning

统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 1,676 篇被归入该主题的论文。

主要结论

每年论文数

各会议论文数

按年份和会议统计

通用机器学习 · reinforcement learning论文数，按年份和会议
年份NeurIPSICLRICMLCVPR合计
202314666723287
2024101216824403
2025108230712411
202611333511314575

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

全部通用机器学习 · reinforcement learning论文

年份全部2023202420252026
会议全部NeurIPSICLRICMLCVPR

共 403 篇，第 3 / 9 页

  1. ICLR 2024Making RL with Preference-based Feedback Efficient via Randomization
  2. ICLR 2024MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
  3. ICLR 2024Massively Scalable Inverse Reinforcement Learning in Google Maps
  4. ICLR 2024Mastering Memory Tasks with World Models
  5. ICLR 2024Maximum Entropy Heterogeneous-Agent Reinforcement Learning
  6. ICLR 2024Maximum Entropy Model Correction in Reinforcement Learning
  7. ICLR 2024Mediator Interpretation and Faster Learning Algorithms for Linear Correlated Equilibria in General Sequential Games
  8. ICLR 2024Memory-Consistent Neural Networks for Imitation Learning
  9. ICLR 2024Meta Inverse Constrained Reinforcement Learning: Convergence Guarantee and Generalization Analysis
  10. ICLR 2024Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer
  11. ICLR 2024METRA: Scalable Unsupervised RL with Metric-Aware Abstraction
  12. ICLR 2024Motif: Intrinsic Motivation from Artificial Intelligence Feedback
  13. ICLR 2024Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts
  14. ICLR 2024Negatively Correlated Ensemble Reinforcement Learning for Online Diverse Game Level Generation
  15. ICLR 2024ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update
  16. ICLR 2024Off-Policy Primal-Dual Safe Reinforcement Learning
  17. ICLR 2024Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees
  18. ICLR 2024Offline RL with Observation Histories: Analyzing and Improving Sample Complexity
  19. ICLR 2024OMNI: Open-endedness via Models of human Notions of Interestingness
  20. ICLR 2024On Differentially Private Federated Linear Contextual Bandits
  21. ICLR 2024On Double Descent in Reinforcement Learning with LSTD and Random Features
  22. ICLR 2024On Stationary Point Convergence of PPO-Clip
  23. ICLR 2024On the Expressivity of Objective-Specification Formalisms in Reinforcement Learning
  24. ICLR 2024On the Hardness of Constrained Cooperative Multi-Agent Reinforcement Learning
  25. ICLR 2024On the Role of General Function Approximation in Offline Reinforcement Learning
  26. ICLR 2024On Trajectory Augmentations for Off-Policy Evaluation
  27. ICLR 2024Online Information Acquisition: Hiring Multiple Agents
  28. ICLR 2024Optimal Sample Complexity for Average Reward Markov Decision Processes
  29. ICLR 2024PAE: Reinforcement Learning from External Knowledge for Efficient Exploration
  30. ICLR 2024PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback
  31. ICLR 2024Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning
  32. ICLR 2024Physics-Regulated Deep Reinforcement Learning: Invariant Embeddings
  33. ICLR 2024Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning
  34. ICLR 2024Policy Rehearsing: Training Generalizable Policies for Reinforcement Learning
  35. ICLR 2024Pre-Training Goal-based Models for Sample-Efficient Reinforcement Learning
  36. ICLR 2024Pre-training with Synthetic Data Helps Offline Reinforcement Learning
  37. ICLR 2024Prioritized Soft Q-Decomposition for Lexicographic Reinforcement Learning
  38. ICLR 2024Privileged Sensing Scaffolds Reinforcement Learning
  39. ICLR 2024Proper Laplacian Representation Learning
  40. ICLR 2024Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
  41. ICLR 2024Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes
  42. ICLR 2024Provable Offline Preference-Based Reinforcement Learning
  43. ICLR 2024Provable Reward-Agnostic Preference-Based Reinforcement Learning
  44. ICLR 2024Provably Efficient CVaR RL in Low-rank MDPs
  45. ICLR 2024Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback
  46. ICLR 2024Provably Efficient UCB-type Algorithms For Learning Predictive State Representations
  47. ICLR 2024Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
  48. ICLR 2024Quantifying the Sensitivity of Inverse Reinforcement Learning to Misspecification
  49. ICLR 2024Query-Policy Misalignment in Preference-Based Reinforcement Learning
  50. ICLR 2024Reasoning with Latent Diffusion in Offline Reinforcement Learning

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常见问题

2023–2026 年四大会议一共有多少篇通用机器学习 · reinforcement learning论文？
共 1,676 篇，按年份分别是 2023 年 287 篇、2024 年 403 篇、2025 年 411 篇、2026 年 575 篇。
NeurIPS 2026 有多少篇通用机器学习 · reinforcement learning论文？
NeurIPS 2026 有 113 篇通用机器学习 · reinforcement learning论文，上一届是 108 篇。
ICLR 2026 有多少篇通用机器学习 · reinforcement learning论文？
ICLR 2026 有 335 篇通用机器学习 · reinforcement learning论文，上一届是 230 篇。
ICML 2026 有多少篇通用机器学习 · reinforcement learning论文？
ICML 2026 有 113 篇通用机器学习 · reinforcement learning论文，上一届是 71 篇。
CVPR 2026 有多少篇通用机器学习 · reinforcement learning论文？
CVPR 2026 有 14 篇通用机器学习 · reinforcement learning论文，上一届是 2 篇。
哪个会议的通用机器学习 · reinforcement learning论文最多？
ICLR，2023–2026 年共 847 篇。

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