Python Deep Learning

Quick recap on reinforcement learning

We first encountered reinforcement learning in Chapter 1, Machine Learning – An Introduction, when we looked at the three different types of learning processes: supervised, unsupervised, and reinforcement. In reinforcement learning, an agent receives rewards within an environment. For example, the agent might be a mouse in a maze and the reward might be some food somewhere in that maze. Reinforcement learning can sometimes feel a bit like a supervised recurrent network problem. A network is given a series of data and must learn a response.

The key distinction that makes a task a reinforcement learning problem is that the responses the agent gives changes the data it receives in future time steps. If the mouse ...