错峰和大家分享一下我们最近发表在NeurIPS’24的oral 工作,《Policy learning from Tutorial Books via Understanding,Rehearsing and Introspecting》,本文也是我们的oral presentation的修改文稿 为什么要从书里学策略 近年来,使用基于大型语言模型(LLM)的智能体,即LLM-a
【RLChina论文研讨会】第102期 陈雄辉 Policy Learning from Tutorial Books via Understanding, R, 视频播放量 261、弹幕量 0、点赞数 5、投硬币枚数 0、收藏人数 11、转发人数 0, 视频作者 RLChina强化学习社区, 作者简介 ,相关视频:【RLChina论文研讨会】第102期 Trist
Relay Policy Learning是一种通过模仿学习和强化学习解决长时程机器人任务的方法。这种方法包括两个阶段:模仿学习阶段和强化学习阶段。在模仿学习阶段,通过使用无结构人类演示作为额外的监督信息,生成目标条件的层级策略。在强化学习阶段,对这些策略进行微调以提高任务性能。相对于传统的层级强化学习方法,Relay Policy Learnin...
TheIBCbaseline is adapted fromKevin Zakka's reimplementation. TheRobomimictasks andObservationEncoderare used extensively in this project. ThePush-Ttask is adapted fromIBC. TheBlock Pushingtask is adapted fromBETandIBC. TheKitchentask is adapted fromBETandRelay Policy Learning. ...
《reinforcement learning:an introduction》第十三章《Policy Gradient Methods》总结 :policygradient方法是通过计算policyπ(a|s;θ)的gradient来更新policy的参数θ,从而优化policy;那么衡量policy好坏的指标J...由于组里新同学进来,需要带着他入门RL,选择从silver的课程开始。对于我自己,增加一个仔细阅读《reinforcement...
Machine learningOptimal controlPolicy iterationNonlinear systemsChemical process controlReinforcement learning (RL) has been a powerful framework for designing optimal controllers for nonlinear systems. This tutorial review provides a comprehensive exploration of RL techniques, with a particular focus on policy...
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强化学习是通过奖惩的反馈来不断学习的,在Q-Learning,Sarsa和DQN中,都是学习到了价值函数或对价值函数的近似,然后根据价值来选择策略(如选择最大价值的动作),所以这一类也被称为Value Based Model。但是这种处理方式有几处瓶颈: 处理连续动作效果差。对于高维度或连续状态空间,使用Value Based通过得到价值函数再制定...
In this tutorial, we’ll examine two different approaches to training a reinforcement learning agent: on-policy learning and off-policy learning. We’ll start by revisiting what they’re supposed to solve and determining each one’s advantages or disadvantages. ...
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