Reinforcement Learning with TensorFlow

Summary

In this chapter, we discussed real strategy games and why researchers from the AI community are trying to solve them. We also covered the complexity and properties of real strategy games and the different traditional AI approaches, such as case-based reasoning and online case-based planning to solve them and their drawbacks. We discussed the reason behind reinforcement learning being the perfect candidate for the problem and how it is successful in fulfilling the complexity and issues related to real-time strategy games where earlier traditional AI approaches failed. We also learnt about deep autoencoders and how they can be used to reduce the dimensionality of the input data and obtain a better representation of the input.

In the ...