According to Wikipedia's definition of reinforcement learning: reinforcement learning (rl) is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions. If a (s, a) takes the advantage function or q (s, a) or their estimated values, it is the parameter update process of the pg class rl algorithm. It can be seen that rl has certain preferences for data to weight the policy gradient. Below are some rl+il articles I have read, mostly. As a side note, openai’s introductory tutorial on reinforcement learning (rl) is called spinning up. Openai said that humans with no basis in machine learning can quickly get started with reinforcement learning. There are concepts, there are implementation codes for a series of key algorithms, and there are practices.
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