Applied Reinforcement Learning with Python by Taweh Beysolow II

Author:Taweh Beysolow II
Language: eng
Format: epub
ISBN: 9781484251270
Publisher: Apress

With this now explained, we can discuss Double Q Networks and how they are being utilized to overcome the shortcomings of Deep Q Networks. Rather than add additional models, we instead utilize the target network to estimate the value while utilizing the online network to evaluate the explore-exploit decision-making process. The target function for the double Q network is the following:

Conclusion

With both examples of Q learning and Deep Q Learning finished, we advise the reader to try applying these algorithms in a variety of contexts. Where necessary, they can change parameters and fork/change existing code and models. Regardless, what I would suggest to readers to keep in mind moving forward is the following:Q learning is straightforward and easy to explain – The benefit to this algorithm is that it is easy to understand why the Q values are inputted as such. For tasks where implementing algorithms requires transparency, it is not unwise to consider something like this for where it will do.

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