Learning benefits evolution if sex gives pleasure

In this paper the effects of individual learning on an evolving population of situated agents are investigated. We work with a novel type of system where agents can decide autonomously (by their controllers) if/when they reproduce and the bias in the agent controllers for the mating action is adaptable by individual learning. Our experiments show that in such a system reinforcement learning with the straightforward rewards system based on energy makes the agents lose their interest in mating. In other words, we see that learning frustrates evolution, killing the whole population on the long run. This effect can be counteracted by introducing a specially designated positive mating reward, pretty much like an orgasm in Nature. With this twist individual learning becomes a positive force. It can make the otherwise disappearing population viable by keeping agents alive that did not yet learn the task at hand. This hiding effect proves positive for it provides a smooth road for the population to adapt and learn the task with a lower risk of extinction.