Hands-On Q-Learning with Python

The learning parameters – alpha, gamma, and epsilon

Here's an updated version of the Bellman equation:

Compare it to the version we used in the last section:

In the new version, we've added in an alpha term, which means we need to include the current Q-value of the state-action pair and discount it by the alpha value.

The first equation is telling us that the new Q-value (the right side of the equation) of our state-action pair is equal to the old Q-value plus the current reward and the discounted future reward, minus the old Q-value multiplied ...