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  2. Reinforcement
  • IntelliLight: a Reinforcement Learning Approach for Intelligent Traffic Light Control 论文阅读

    IntelliLight 全文脉络概述1、本文贡献1)Experiments with real traffic data.2)Interpretations of the policy.3&am
    论文learningApproachIntelliLightReinforcement
    admin4月前
    340
  • A Minimalist Approach to Offline Reinforcement Learning[TD3+BC]阅读笔记

    A Minimalist Approach to Offline Reinforcement Learning[TD3BC]阅读笔记 文章目录A Minimalist Approach to Offline Reinforcement Le
    笔记OfflineApproachMinimalistReinforcement
    admin4月前
    390
  • [NIPS2017] A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning 笔记

    文章目录前言Background and Related WorkNeural Fictitious Self-PlayPolicy-Space Response OraclesMeta-Strategy SolversDeep Cogni
    笔记GAMETheoreticUnifiedReinforcement
    admin4月前
    300
  • 【论文翻译】A Comprehensive Survey on Safe Reinforcement Learning

    本篇译文为方便自己再次阅读而记录,源自Google翻译和CNKI翻译助手。习惯用语保持英文(例:agent),一些细微之处结合自己
    论文ComprehensiveSurveylearningReinforcement
    admin6月前
    900
  • 《A Distributional Perspective on Reinforcement Learning》的理解

    近日看一本关于Reinforcement Learning的入门书《Deep Reinforcement Learning Hands On》,甚有收获。该书由PacktPublishing在2018年出版,它不仅介绍了RL的基础理论,而且
    perspectiveDistributionallearningReinforcement
    admin6月前
    600
  • A Distributional Perspective on Reinforcement Learning

    本文论证了值分布的基本重要性:强化学习智能体收到的随机回报的分布。这与强化学习的常见方法相反,后者对这种回报或价值的期望进行建模。尽管已经建立了研究价值分布的文献体系,但迄今为止&#xff
    perspectiveDistributionallearningReinforcement
    admin6月前
    800
  • Human-level control through deep reinforcement learning

    Abstract 强化学习理论在动物行为上,深入到心理和神经科学的角度,关于在一个环境中如何使得智能体优化他们的控制,提供了一个正式的规范。为了利用强化学习成功的接近现实世界
    controlLevelhumanlearningReinforcement
    admin7月前
    880
  • 深度强化学习综述论文 A Brief Survey of Deep Reinforcement Learning

    A Brief Survey of Deep Reinforcement Learning 深度强化学习的简要概述 作者: Kai Arulkumaran, Marc Peter Deisenroth, Miles
    深度论文SurveylearningReinforcement
    admin8月前
    830
  • Reinforcement Learning with Human in the Loop & Human Feedback

    人在环路的强化学习(Reinforcement Learning with Human in the Loop, HIL) 和 人类反馈的强化学习(Reinforcement
    humanlearningReinforcementfeedbackamp
    admin8月前
    500
  • 大模型微调实战之 Transformer 强化学习(TRL Reinforcement Learning)(三)Proximal Policy Optimization

    大模型微调实战之 Transformer 强化学习(TRL Reinforcement Learning)(三)Proximal Policy Optimization Proximal Policy Optimization 这是一个
    实战模型TRLTransformerReinforcement
    admin8月前
    430
  • Deep Reinforcement Learning + Potential Game + Vehicular Edge Computing

    文献 [1] 采用deep reinforcement learning和potential game研究vehicular edge computing场景下的任务卸载和资源优化分配策略 文献[2] 采用potential game设计
    learningpotentialdeepReinforcementedge
    admin8月前
    570
  • Reinforcement

    Reinforcement
    admin2023-7-1
    660
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