Deciding on synchronous and asynchronous actors

We started off this book with a simple discussion of what artificial general intelligence (AGI) is. In short, AGI is our attempt at generalizing an intelligent system to solve multiple tasks. RL is often thought of as a stepping stool to AGI primarily because it tries to generalize state-based learning. While both RL and AGI take deep inspiration from how we think, be it rewards or possibly consciousness itself, the former tends to incorporate direct analogies. The actor-critic concept in RL is an excellent example of how we use an interpretation of human psychology to create a form of learning. For instance, we humans often consider the consequences of our actions and determine the advantages they may or may not give us. This example is perfectly analogous to actor-critic and advantage methods we use in RL. Take this further and...