Coevolution of neural networks for agents and environments.

Evolutionary strategies have recently shown great results in the field of policy search. Compared to some classical artificial neural networks used in reinforcement learning, evolutionary strategies generate populations of agents which are evaluated on a specific task. The algorithm detailed here demonstrates how artificial neural networks can be evolved in a process of neuroevolution and used as agents in the 2D-game Zelda, producing relevant behaviors. Moreover, to increase the diversity and quantity of available maps, this paper shows how it is possible to generate environments using evolutionary strategies and neuroevolution, as well as neural cellular automata. Finally, to evolve populations of environments and agents cohesively, a coevolution algorithm was developed. Results demonstrate the potential of coevolution in the field of videogames by creating a wide range of diverse environments, and by creating agent strategies to solve these levels. However, these results also highlight the complexity of continuously generating novelty as agents and maps tend to converge quickly on similar patterns.