强化学习论文趋势 2023–2026

策略优化、离线 RL 与基于环境的决策学习

统计范围:NeurIPS、ICLR、ICML、CVPR 四个会议,2023–2026 年,共 6,885 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

强化学习论文数,按年份和会议
年份NeurIPSICLRICMLCVPR合计
2023484233297561,070
2024466304356641,190
2025784379405721,640
20267609111,0212932,985

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

全部强化学习论文

共 1,190 篇,第 24 / 24 页

  1. NeurIPS 2024The Sample-Communication Complexity Trade-off in Federated Q-Learning
  2. NeurIPS 2024The surprising efficiency of temporal difference learning for rare event prediction
  3. NeurIPS 2024The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning
  4. NeurIPS 2024The Value of Reward Lookahead in Reinforcement Learning
  5. NeurIPS 2024Time-Constrained Robust MDPs
  6. NeurIPS 2024Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning
  7. NeurIPS 2024Towards Effective Planning Strategies for Dynamic Opinion Networks
  8. NeurIPS 2024Towards Next-Generation Logic Synthesis: A Scalable Neural Circuit Generation Framework
  9. NeurIPS 2024Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning
  10. NeurIPS 2024Towards Visual Text Design Transfer Across Languages
  11. NeurIPS 2024Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear $q^\pi$-Realizability and Concentrability
  12. NeurIPS 2024Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models
  13. NeurIPS 2024Transition Constrained Bayesian Optimization via Markov Decision Processes
  14. NeurIPS 2024Truncated Variance Reduced Value Iteration
  15. NeurIPS 2024TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models
  16. NeurIPS 2024Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning
  17. NeurIPS 2024UltraMedical: Building Specialized Generalists in Biomedicine
  18. NeurIPS 2024Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions
  19. NeurIPS 2024Understanding and Improving Training-free Loss-based Diffusion Guidance
  20. NeurIPS 2024Understanding Model Selection for Learning in Strategic Environments
  21. NeurIPS 2024Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning
  22. NeurIPS 2024Unlock the Intermittent Control Ability of Model Free Reinforcement Learning
  23. NeurIPS 2024Using Unity to Help Solve Reinforcement Learning
  24. NeurIPS 2024Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training
  25. NeurIPS 2024Variational Delayed Policy Optimization
  26. NeurIPS 2024Verifiably Robust Conformal Prediction
  27. NeurIPS 2024Verified Safe Reinforcement Learning for Neural Network Dynamic Models
  28. NeurIPS 2024Vision-Language Navigation with Energy-Based Policy
  29. NeurIPS 2024Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes
  30. NeurIPS 2024WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control
  31. NeurIPS 2024When LLM Meets DRL: Advancing Jailbreaking Efficiency via DRL-guided Search
  32. NeurIPS 2024When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL
  33. NeurIPS 2024When Your AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback
  34. NeurIPS 2024WizardArena: Post-training Large Language Models via Simulated Offline Chatbot Arena
  35. NeurIPS 2024WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment
  36. NeurIPS 2024Worst-Case Offline Reinforcement Learning with Arbitrary Data Support
  37. NeurIPS 2024Would I Lie To You? Inference Time Alignment of Language Models using Direct Preference Heads
  38. NeurIPS 2024XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
  39. NeurIPS 2024Zero-Shot Reinforcement Learning from Low Quality Data
  40. NeurIPS 2024ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordination

在 Claude、Cursor 里直接问这个趋势

接上 Paperlist MCP 后,写综述、挑方向或准备投稿时,不用离开对话框就能拿到这页的数字:每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文,AI 会按你的问题整理。

可以直接复制这句话问 AI

用 paperlist 查强化学习在 NeurIPS 2023–2026 的论文趋势,哪一年涨得最快?

AI 会调用的工具:get_topic_stats {"topic": "reinforcement-learning"}

两分钟接入 Paperlist MCP(Claude Code、Cursor、Codex、ChatGPT)

常见问题

2023–2026 年四大会议一共有多少篇强化学习论文?
共 6,885 篇,按年份分别是 2023 年 1,070 篇、2024 年 1,190 篇、2025 年 1,640 篇、2026 年 2,985 篇。
NeurIPS 2026 有多少篇强化学习论文?
NeurIPS 2026 有 760 篇强化学习论文,上一届是 784 篇。
ICLR 2026 有多少篇强化学习论文?
ICLR 2026 有 911 篇强化学习论文,上一届是 379 篇。
ICML 2026 有多少篇强化学习论文?
ICML 2026 有 1,021 篇强化学习论文,上一届是 405 篇。
CVPR 2026 有多少篇强化学习论文?
CVPR 2026 有 293 篇强化学习论文,上一届是 72 篇。
哪个会议的强化学习论文最多?
NeurIPS,2023–2026 年共 2,494 篇。