weixin_44495738的博客

强化学习实践 | DQN1. 直觉介绍2. Experience replay 和 Fixed Q-targets3. 伪代码4. PyTorch实现4.1 CartPole介绍4.2 Dummy Policy4.3 DQN4.4 完整代码(附注释)：原论文：Playing Atari with Deep Reinforcement Learning参考：https://mofanpy.com/tutorials/machine-learning/reinforcement-learning/int

强化学习实践 | Sarsa 例子Q-Learning和Sarsa的区别Sarsa 伪代码二维走迷宫的例子：Sarsa(λ\lambdaλ)参考：https://mofanpy.com/tutorials/machine-learning/reinforcement-learning/tabular-sarsa2/Q-Learning和Sarsa的区别epsilon-greedy使用时机不一样：Q-Learning用epsilon-Greedy选择当前时刻(s)的动作。Sarsa用epsilon-G

强化学习 Q-Learning 几个例子Q-Learning 算法回顾1. 一维找宝藏总结：2. 二维总结：参考：例子1:https://mofanpy.com/tutorials/machine-learning/reinforcement-learning/general-rl/例子2:https://mofanpy.com/tutorials/machine-learning/reinforcement-learning/tabular-q1/Q-Learning 算法回顾初始化Q表

David Silver Lecture 6 | Value Function Approximation1. Introduction2. Incremental Methods2.1 Value Function 预测 | Gradient Descent2.1.1 v^(S,w)\hat{v}(S,w)v^(S,w): Feature Vectors | Linear Function ApproximationTable Lookup Features2.1.2 vπ(S)v_\pi(S)vπ​(S

David Silver Lecture 5 | Model-Free Control1 Introduction1.1 On-policy Learning1.2 Off-policy Learning2 On-Policy Monte-Carlo Control2.1 Generalised Policy Iteration - MC Policy Iteration2.2 ϵ−\epsilon-ϵ−Greedy2.3 GLIE (Greedy in the Limit with Infinite Ex

David Silver Lecture 4 | Model-Free Prediction1. Introduction2. Monte-Carlo Learning2.1 First-Visit Monte-Carlo Policy Evaluation2.2 Every-Visit Monte-Carlo Policy Evaluation例子：21点 Blackjack3. Temporal-Difference Learning4. TD(λ)(\lambda)(λ)参考：https://zhu

David Silver Lecture 3 | 动态规划寻找最优策略1 Introduction2 Policy Evaluation | 得到一个Policy下不同状态的Value2.1 Iterative Policy Evaluation例子：Small Grid World ｜ 给定一个策略，估计该策略下不同State的Value3 Policy Iteration ｜ 找到MDP下最优的Policy3.1 How to improve a policy4 Value Iteration5 Ext

David Silver Lecture 2｜马尔科夫决策过程1. Markov Processes1.0 Introduction1.1 Markov Property1.2 Markov Chain例子：2 Markov Reward Processes (MRP)2.1 Markov Reward Processes (MRP) | 引入了R和Discount Factor例子：2.2 Return Gt [是一个随机变量]2.3 Value Function v(s) [是Gt的期望]例子：2.4

David Silver Course 1 | 深度强化算法学习 +项目讲解0. 强化学习特点1.1 Reward1.2 Sequential Decision Making1.3 History & State1.4 Fully / Partial Observable Environments2.0 Major Components of an RL agent2.1 Policy2.2 Value Function (给定一个Policy之下评估一个状态)2.3 ModelMaze Examp