Evolutionary computation versus reinforcement learning

Many applications of reinforcement learning (RL) and evolutionary computation (EC) are addressing the same problem, namely, to maximize some agent´s fitness function in a potentially unknown environment. The most challenging open issues in such applications include partial observability of the agent´s environment, hierarchical and other types of abstract credit assignment, and the learning of credit assignment algorithms. I summarize why EC provides a more natural framework for addressing these issues than RL based on value functions and dynamic programming. Then I point out fundamental drawbacks of traditional EC methods in case of stochastic environments, stochastic policies, and unknown temporal delays between actions and observable effects. I discuss a remedy called the success-story algorithm which combines aspects of RL and EC