Imagine having an hyper realistic "SimCity" simulation that would show us the best way to rule a city. Wouldn't that be great? What if our SimCity's president was an AI? What would it try to optimise? What would the citizens of the city try to optimise if they were AI too? In this post, we will explore how such a simulator could work.
ReadWe introduce the first reinforcement learning algorithm able to mimic the evolutionary process in an open-ended nature-like environment. Our algorithm searches for policies with increasing evolutionary success by taking into account every state and action each agent goes through its lifetime. In contrast, current evolutionary algorithms discard this information and consequently limit their potential efficiency at tackling sequential decision problems. We test our algorithm in two bio-inspired environments and unravel the interestingly dark evolutionary history of these worlds.
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