Imagine for a moment that your data is not a discrete body of text or a carefully cleaned set of records from your organization's data warehouse. Perhaps you would like to train an agent to navigate an environment. How would you begin to solve this problem? None of the techniques that we have covered so far are suitable for such a task. We need to think about how we can train our model in quite a different way to make this problem tractable. Additionally, with use cases where the problem can be framed as an agent exploring and attaining a reward from an environment, from game playing to personalized news recommendations, Deep Q-Networks (DQNs) are useful tools in our arsenal of deep learning techniques.
Reinforcement learning (RL) has been described by Yann LeCun (who was instrumental in the development of Convolutional Neural Networks (CNNs...
