强化学习 - 标签 - 凯鲁嘎吉 - 博客园

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《Reinforcement Learning: An Overview》中文版发布（《强化学习：概述》）凯鲁嘎吉 2025-12-31 15:02 阅读:37 评论:0 推荐:0

RL——METRA: Scalable Unsupervised RL with Metric-Aware Abstraction 凯鲁嘎吉 2025-12-03 21:32 阅读:32 评论:0 推荐:0

Python小练习：object类型数据加载凯鲁嘎吉 2023-04-12 22:08 阅读:146 评论:0 推荐:0

Safe RL——Constrained Policy Optimization (CPO) 凯鲁嘎吉 2022-11-19 10:44 阅读:2140 评论:0 推荐:1

Safe RL——Constrained Variational Policy Optimization for Safe Reinforcement Learning (CVPO) 凯鲁嘎吉 2022-09-04 10:44 阅读:1386 评论:1 推荐:0

《Easy RL》面试题汇总凯鲁嘎吉 2022-04-13 16:22 阅读:2729 评论:0 推荐:0

离线强化学习(A Survey on Offline Reinforcement Learning) 凯鲁嘎吉 2022-03-22 17:18 阅读:3848 评论:5 推荐:3

Deep Reinforcement Learning Hands-On——Policy Gradients – an Alternative 凯鲁嘎吉 2022-02-28 16:32 阅读:2089 评论:0 推荐:0

动态规划(Dynamic Programming, DP) 凯鲁嘎吉 2022-02-26 09:36 阅读:584 评论:0 推荐:0

Deep Reinforcement Learning Hands-On——Higher-Level RL Libraries (PTAN) 凯鲁嘎吉 2022-02-23 12:01 阅读:485 评论:0 推荐:0

Deep Reinforcement Learning Hands-On——Tabular Learning and the Bellman Equation 凯鲁嘎吉 2022-02-14 10:04 阅读:347 评论:0 推荐:0

Hands-On Reinforcement Learning With Python——Temporal Difference Learning 凯鲁嘎吉 2022-01-10 09:58 阅读:328 评论:0 推荐:0

Windows下OpenAI gym环境的使用凯鲁嘎吉 2022-01-04 01:06 阅读:1698 评论:0 推荐:1

Meta-RL——Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables 凯鲁嘎吉 2021-11-23 13:04 阅读:1339 评论:10 推荐:2

RL——Deep Reinforcement Learning amidst Continual/Lifelong Structured Non-Stationarity 凯鲁嘎吉 2021-11-16 17:26 阅读:824 评论:0 推荐:0

Meta-RL——Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices 凯鲁嘎吉 2021-11-10 15:22 阅读:389 评论:0 推荐:0

强化学习相关资料(书籍，课程，网址，笔记等) 凯鲁嘎吉 2021-11-01 16:14 阅读:3089 评论:2 推荐:1

双层优化问题：统一GAN，演员-评论员与元学习方法(Bilevel Optimization Problem unifies GAN, Actor-Critic, and Meta-Learning Methods) 凯鲁嘎吉 2021-10-21 13:15 阅读:5942 评论:2 推荐:1

重要性采样(Importance Sampling)——TRPO与PPO的补充凯鲁嘎吉 2021-10-13 11:32 阅读:2688 评论:0 推荐:2

近端策略优化算法(Proximal Policy Optimization Algorithms, PPO) 凯鲁嘎吉 2021-10-12 09:54 阅读:3322 评论:0 推荐:0