Deep Learning with TensorFlow

Summary

This chapter covers the basic principles of Reinforcement Learning and the fundamental Q-learning algorithm.

The distinctive feature of Q-learning is its capacity to choose between immediate rewards and delayed rewards. Q-learning at its simplest uses tables to store data. This very quickly loses viability as the state/action space of the system it is monitoring/controlling increases.

We can overcome this problem by using a neural network as a function approximator, which takes the state and action as input, and outputs the corresponding Q-value.

Following this idea, we implemented a Q-learning neural network using the TensorFlow framework and the OpenAI Gym toolkit for developing and comparing Reinforcement Learning algorithms.