Reinforcement Learning Basics [1]: Basic knowledge points, Markov decision process, Monte Carlo strategy gradient theorem, REINFORCE algorithm - Code World

Reinforcement Learning Basics [1]: Basic knowledge points, Markov decision process, Monte Carlo strategy gradient theorem, REINFORCE algorithm

Enterprise 2023-06-04 22:30:23 views: null

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[Reinforcement Learning Principles + Project Column] Must-see series: single-agent, multi-agent algorithm principles + project practice, related skills (parameter adjustment, drawing, etc., interesting project realization, academic application project realization

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Column details : [Reinforcement Learning Principles + Project Column] Must-see series: single-agent, multi-agent algorithm principles + project practice, related skills (parameter adjustment, drawing, etc., interesting project realization, academic application project realization

The plan for deep reinforcement learning is:

Basic single-intelligence algorithm teaching (gym environment-based)
Mainstream multi-intelligence algorithm teaching (gym environment-based)
Some interesting projects (Super Mario, playing backgammon, Fight the Landlord, various game applications)
Actual combat of single-intelligence and multiple-intelligence questions (the paper reproduces partial business such as: UAV optimization scheduling, power resource scheduling and other project applications)

This column is mainly to facilitate entry-level students to quickly grasp reinforcement learning single-agent | multi-agent algorithm principles + project practice. In the follow-up, we will continue to analyze the knowledge principles involved in deep learning to everyone, so that everyone can reserve knowledge while practicing the project, knowing what it is, why it is, and knowing why to know why it is. <

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Origin blog.csdn.net/sinat_39620217/article/details/131004750

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