Reinforcementlearning
![[David Silver] 6. Value Function Approximation: Experiment Replay, Deep Q-Network (DQN)](https://img1.daumcdn.net/thumb/R750x0/?scode=mtistory2&fname=https%3A%2F%2Fblog.kakaocdn.net%2Fdn%2FczmGA9%2FbtrzcRJ5dBF%2FwiznIbR8XpxQIMj9PP7Apk%2Fimg.jpg)
[David Silver] 6. Value Function Approximation: Experiment Replay, Deep Q-Network (DQN)
이 글은 필자가 David Silver의 Reinforcement Learning 강좌를 듣고 정리한 글입니다. This lecture suggests the solution for large MDPs using function approximation.We have to scale up the model-free methods for prediction and control. So for lecture 6 and 7, we will learn how can we scale up the model-free methods. How have we dealt with small (not large) MDPs so far? We have represented the value function by a looku..
![[David Silver] 1. Introduction to Reinforcement learning](https://img1.daumcdn.net/thumb/R750x0/?scode=mtistory2&fname=https%3A%2F%2Fblog.kakaocdn.net%2Fdn%2FBJ8BY%2Fbtryg4VPIum%2FFvGmkyvcQbnUcdyguPXO60%2Fimg.png)
[David Silver] 1. Introduction to Reinforcement learning
이 글은 필자가 David Silver의 Reinforcement Learning 강좌를 듣고 정리한 글입니다. 🧵 Sequential decision making Goal: select actions to maximize total future reward To maximize total future reward, there might be tasks that long-term reward matters, so it may be better to sacrifice immediate reward to gain more long-term reward. Reward may be delayed. Solution of Sequential decision making Reinforcement Learning Pl..