【Valse 2023】Learning Environment Models for Reinforcement Learning——俞扬老师 针对强化学习的环境模型学习 辛苦小工具人 模仿学习的局限性:数据分布不一样,总会有泛化误差 比如自动驾驶学会了专家的决策,刚开始和专家一样开车,但如果中间有一点点变化,则决策产生偏差,状态就有更大的偏差,越来越偏 累积误差到底有...
Yuxi Li, Iterative improvements from feedback for language models, ScienceOpen, 2023 Yuxi Li, Deep Reinforcement Learning: An Overview, arXiv, 2017 (150 pages, 1900+ citations) Yuxi Li, RL in Practice: Opportunities and Challenges, arXiv, 2022 顺便说一下 世界属于专家的 各行各业的专家 AI...
4 Learning Representations for Control with Bisimulation Metrics 我们提出了用于控制的深度互模拟(DBC),这是一种从非结构化、高维状态学习控制策略的数据高效方法。与之前的互模拟工作相反,该工作通常旨在学习状态之间形式为 的距离函数,我们的目标是学习表征Z,在表征Z下l1距离对应于互模拟度量,然后使用这些表征来改进强...
斯坦福大学《强化学习|Stanford CS234 Reinforcement Learning 2024》deepseek翻译 275 0 16:01:35 App 斯坦福大学《社会和经济网络:建模与分析|Social and Economic Networks: Models and Analysis》中英字幕 3391 0 12:23:38 App 吴恩达@斯坦福大学《深度学习|Stanford CS230: Deep Learning | Autumn 2018》中英...
Reward Modelling(RM)and Reinforcement Learning from Human Feedback(RLHF)for Large language models(LLM)技术初探 一、RLHF 技术的背景 OpenAI 推出的 ChatGPT 对话模型掀起了新的 AI 热潮,它面对多种多样的问题对答如流,似乎已经打破了机器和人的边界。这一工作的背后是大型语言模型 (Large Language Model,...
A curated list of reinforcement learning with human feedback resources (continually updated) reinforcement-learning deep-learning deep-reinforcement-learning large-language-models human-feedback rlhf Updated Feb 19, 2025 Load more… Improve this page Add a description, image, and links to the ...
Peter DayanHiroyuki NakaharaDayan, P., & Nakahara, H. (2018). Models and methods for reinforcement learning. In J. Wixted & E.-J. Wagenmakers (Eds.), The Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience. Volume 5: Methodology (Fourth ed.). John Wiley & Sons....
论文Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning 的阅读。本文提出了一种名为VLM-RM的方法,使用预训练的视觉-语言模型(如CLIP)作为强化学习任务的奖励模型,以自然语言描述任务并避免手动设计奖励函数或收集昂贵的数据来学习奖励模型。实验结果显示,通过使用 VLM-RM,可以有效地训练代...
Kernel-based models for reinforcement learning 来自 ResearchGate 喜欢 0 阅读量: 89 作者:NK Jong,P Stone 摘要: Model-based approaches to reinforcement learning exhibit low sample complexity while learning nearly optimal policies, but they are generally restricted to finite domains. Mean-while, function...
(http://v.youku.com/v_show/id_XNDcyOTU3NDc2.html),相比目前最先进的基于Hidden Markov Model的技术,其准确率提升了大约30%(If you use that to take it much more data than had previously been able to be used with the hidden markov models, so that one change that particular break through ...