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ByteByteAI：RLHF专题，结合工具调用与推理。
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CodeTradin：Deep RL专题，结合工具调用与推理。
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Sequoia Ca：Deep RL专题，结合工具调用与推理。
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freeCodeCa：PPO专题，改进偏好对齐与安全。
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Stanford O：Deep RL专题，讲解原理与上手路径。
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AI Fish Evolve through Reinforcement Learning with Genetic Algorithms #programming #ai
CodeCrafte：围绕Deep RL的多模态模型分享。
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AI Researc：Deep RL专题，面向端侧低成本部署。
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AI Enginee：Deep RL专题，结合工具调用与推理。
#22
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AI Enginee：Deep RL专题，结合工具调用与推理。
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Stanford O：围绕Deep RL的多模态模型分享。
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StatQuest ：RLHF专题，改进偏好对齐与安全。
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Stanford O：Deep RL专题，介绍平台接口与集成。
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Stanford O：围绕Deep RL的多模态模型分享。
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Neural Bre：围绕Deep RL的多模态模型分享。
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Johnny Cod：围绕PPO的多模态模型分享。
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Stanford CS224R Deep Reinforcement Learning | Spring 2025 | Lecture 2: Imitation Learning
Stanford O：围绕Deep RL的多模态模型分享。
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Reinforcement Learning explained in simple and easy way! Important Machine Learning topics to know!
Keerti Pur：Deep RL专题，讲解原理与上手路径。
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Stanford CS234 Reinforcement Learning I Q learning and Function Approximation I 2024 I Lecture 4
Stanford O：Deep RL专题，介绍平台接口与集成。
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TODAYS TEC：围绕Deep RL的多模态模型分享。
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Ignacio Ay：围绕DQN的多模态模型分享。
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TutorialsP：Deep RL专题，讲解原理与上手路径。
#69
1. بالعربي المحاضرة الأولى في ال Reinforcement Learning بالعربي | مفاهيم أساسية
ELPRINCE：Deep RL专题，介绍平台接口与集成。
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Depth Firs：PPO专题，扩展长视频时空理解。
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Martin Is ：PPO专题，改进偏好对齐与安全。
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Nathan Lam：围绕Deep RL的多模态模型分享。
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Zachary Hu：围绕Deep RL的多模态模型分享。
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Intrusion Detection using Machine Learning | Multi-Agent Reinforcement Learning | Final Year Project
Ieee Xpert：多智能体专题，结合工具调用与推理。
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Berkeley R：多智能体专题，结合工具调用与推理。
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PPO - Proximal Policy Optimization paper explained in a min. #ppo #trpo #llm #trendingshorts #ainews
Paper in a：PPO专题，讲解原理与上手路径。
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Sergey Levine - Reinforcement Learning in the Age of Foundation Models - RLC 2024
Reinforcem：围绕Deep RL的多模态模型分享。
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Agentic AI MOOC | UC Berkeley CS294-196 F25 | Multi-Agent Systems in Era of LLMs by Oriol Vinyals
Berkeley R：多智能体专题，结合工具调用与推理。
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Stanford CS234 Reinforcement Learning I Multi-Agent Game Playing I 2024 I Lecture 14
Stanford O：多智能体专题，结合工具调用与推理。
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LangGraph:17 Introduction to Multi-Agent System #llm #genai #aiagents #ai #genai #agent
Sunny Savi：多智能体专题，结合工具调用与推理。
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The AI All：多智能体专题，结合工具调用与推理。
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SESSION 2 | Multi-Agent Reinforcement Learning: Foundations and Modern Approaches | IIIA-CSIC Course
IIIA-CSIC：多智能体专题，结合工具调用与推理。
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【人工智能】算力从来不是唯一瓶颈 | PPO之父 | 约翰·舒尔曼 | RLHF架构师 | Thinking Machines | Tinker微调API | 价值函数回归 | 算法巧思 | 强化学习
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Coding Nin：多智能体专题，结合工具调用与推理。
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Tutorial H：DQN专题，结合工具调用与推理。