There is so much love for Artificial Intelligence (AI) at the moment; however what I find so cool is the ’training’ and evolution of different learning algorithms.

This is a collection of different simulation experiments to watch and learn about to further understand machine learning. Starting with Yosh’s project, we witness the implementation of a machine-learning model, specifically Deep-Q-Learning, in teaching AI to drive in the popular game Trackmania. Following this, we explore Pezzza’s thoughtfully crafted simulation depicting the dynamics between predatory and prey AI entities. Our journey then takes us to David R Miller’s project, where he has simulated evolutionary conditions to provide a deeper understanding of natural selection mechanisms. We conclude with Tanmay Kumar Sinha’s evolution simulator that presents an intriguing visualization of organisms adapting to their environment over time. These projects provide a fascinating glimpse into the myriad applications of AI and simulations in understanding complex systems.

Yosh’s AI learning to drive in Trackmania

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Youtube summary: I made an A.I. that teaches itself to drive in the racing game Trackmania, using Machine-Learning. I used Deep-Q-Learning, a Reinforcement Learning algorithm. -Yosh

Pezzza’s Work evolution simulator - predator vs prey

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Youtube summary: Two AI entities, Predators and Prey, try to survive in a simulation with multiple simple variables. Really well programmed and put together. This would be worthwhile learning how to do.

David R Miller’s evolving creatures

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Youtube summary: This is a report of a software project that created the conditions for evolution in an attempt to learn something about how evolution works in nature. This is for the programmer looking for ideas for interdisciplinary programming projects, or for anyone interested in how evolution and natural selection work. Before commenting on the religious/theological implications of this simulation, please note that this video in no way purports to explain all the mysteries of life and the universe. GitHub: https://github.com/davidrmiller/biosim4

Tanmay Kumar Sinha’s evolution simulator

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Youtube summary: Evolution Simulation is a fun project to visualise how organisms evolve according to their environment over a period of time. We have made a simple set of rules as to how the organism may travel, eat, reproduce or die. Every generation has some random variation and the traits of organisms alive are plotted. The Zoo directory contains multiple such environments with variations in rules and organism type. For instance, the Prey-Predator directory has organisms which are either prey or predator and the graph of their populations over time resembles the corresponding differential equations. More such simulations with different rules are to be added.

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