Artificial Intelligence A-Z: Learn How To Build An AI: Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications by Nileriver Publications

Author:Nileriver Publications
Language: eng
Format: azw3, epub, pdf
Published: 2020-10-02T00:00:00+00:00

The advances in algorithms for DL have brought up a new wave of successful applications in Reinforcement Learning, because it offers the opportunity to efficiently work with high dimensional input data (like images). Depending on necessity in this context the trained deep NN can be seen as a kind of end-to-end RL approach, where the agent can learn a state abstraction and a policy approximation directly from its input data.

Does AlphaGo use Deep Q-Learning

It should be distinguished whether the Deep Q-Learning here is referring to 1) the original paper that creates an algorithm called Deep Q-Learning or 2) just Q-Learning with Deep Neural Network and this is very useful for all the purposes. Depending on necessity i will talk about the former since it is a special case of the latter.

It is evident that AlphaGo, of any version, does not use the exact algorithm indicated in Deep Q-Learning paper to give you the best of the result in assertion of progression. Adding further to explain this we can look into the components of these algorithms to see how much they are similar to each other to give you the best of the result in assertion of progression. (Only AlphaGo Zero is considered for the rest.)

Vaguely speaking, an RL algorithm is characterised by how the policy is

represented

used for prediction

improved

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