- Python Reinforcement Learning
- Sudharsan Ravichandiran Sean Saito Rajalingappaa Shanmugamani Yang Wenzhuo
- 123字
- 2021-06-24 15:17:31
Episodic and continuous tasks
Episodic tasks are the tasks that have a terminal state (end). In RL, episodes are considered agent-environment interactions from initial to final states.
For example, in a car racing video game, you start the game (initial state) and play the game until it is over (final state). This is called an episode. Once the game is over, you start the next episode by restarting the game, and you will begin from the initial state irrespective of the position you were in the previous game. So, each episode is independent of the other.
In a continuous task, there is not a terminal state. Continuous tasks will never end. For example, a personal assistance robot does not have a terminal state.
推薦閱讀
- 達夢數據庫編程指南
- Hands-On Machine Learning with Microsoft Excel 2019
- 從零開始學Hadoop大數據分析(視頻教學版)
- Mastering Ninject for Dependency Injection
- App+軟件+游戲+網站界面設計教程
- Effective Amazon Machine Learning
- 云計算與大數據應用
- R數據科學實戰:工具詳解與案例分析(鮮讀版)
- 數據化網站運營深度剖析
- 中國數字流域
- 深入淺出 Hyperscan:高性能正則表達式算法原理與設計
- 云原生數據中臺:架構、方法論與實踐
- Hadoop大數據開發案例教程與項目實戰(在線實驗+在線自測)
- SAS金融數據挖掘與建模:系統方法與案例解析
- 數據庫技術及應用