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Check out the new article: Neural networks made easy (Part 58): Decision Transformer (DT).
We continue to explore reinforcement learning methods. In this article, I will focus on a slightly different algorithm that considers the Agent’s policy in the paradigm of constructing a sequence of actions.
In this series, we have already examined a fairly wide range of different reinforcement learning algorithms. They all use the basic approach:
The sequence is based on the principles of the Markov process. It is assumed that the starting point is the current state of the environment. There is only one optimal way out of this state and it does not depend on the previous path.
Author: Dmitriy Gizlyk