In this chapter, you learned about EAs, a new class of black-box algorithms inspired by biological evolution that can be applied to RL tasks. EAs solve these problems from a different perspective compared to reinforcement learning. You saw that many characteristics that we have to deal with when we design RL algorithms are not valid in evolutionary methods. The differences are in both the intrinsic optimization method and the underlying assumptions. For example, because EAs are black-box algorithms, we can optimize whatever function we want as we are no longer constrained to using differentiable functions, like we were with RL. EAs have many other advantages, as we saw throughout this chapter, but they also have numerous downsides.
Next, we looked at two evolutionary algorithms: genetic algorithms and evolution strategies. Genetic algorithms are more complex as they create...
