Treffer: Self-Learning AI for Flappy Bird using Neuro-Evolution of Augmenting Topologies.

The contribution of this work includes the realization of a self-learning artificial intelligence agent that can play the popular game Flappy Bird through the Neuroevolution of Augmenting Topologies algorithm. NEAT is a neuroevolutionary method based on simultaneously evolving architecture and weights of the neural networks to enable the agent to improve its performance through mechanisms of simulated natural selection and genetic variation. This work aims to evidence the efficiency of using NEAT for training an autonomous agent to play video games with no previous predefined strategy, emphasizing adaptability and learning skills of the evolved neural networks to the environmental dynamics. Python with Pygame has been implemented to simulate the environment of the game; it allows for the population of neural networks to evolve across generations. The experimental results confirm that NEAT efficiently improves gameplay over time by the AI agent, leading to a significant increase in survival time and enhanced scores. Advantages of neuroevolution against traditional methods of reinforcement learning are underlined, among which stands out the capability to find complex topologies of neural networks fitted for the task at hand without being designed by a human. Considering these circumstances, the study postulates that NEAT will be able to contribute to ongoing research efforts in the field of adaptive game bots, autonomous systems, and architectures of evolving artificial intelligence. Possible further work may consider extending the approach described here to more complex games and the exploration of hybrid models that integrate neuro-evolution with deep learning techniques. [ABSTRACT FROM AUTHOR]

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