The cross-entropy method in practice
The cross-entropy method's description is split into two unequal parts: practical and theoretical. The practical part is intuitive in its nature, while the theoretical explanation of why the cross-entropy method works, and what's happening, is more sophisticated.
You may remember that the central and trickiest thing in RL is the agent, which is trying to accumulate as much total reward as possible by communicating with the environment. In practice, we follow a common machine learning (ML) approach and replace all of the complications of the agent with some kind of nonlinear trainable function, which maps the agent's input (observations from the environment) to some output. The details of the output that this function produces may depend on a particular method or a family of methods, as described in the previous section (such as value-based versus policy-based methods). As our cross-entropy method is policy-based, our nonlinear...
