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FuturePlans.md

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The future of project-origin

This project is a seed for a far-reaching vision. In the future, it should allow developing a wide variety of universes. Below are some examples of future capabilities of project-origin:

  • project-origin should allow extending the physics of the environment easily and maybe incorporate realistic physical simulators.
  • It should support morphology of creatures, i.e. to give physical shape to creatures, that may affect their capabilities such as movement speed, power, etc.
  • It should support the introduction of different inanimated and non-intelligent living objects with a variety of functionalities and behavior to the universe, e.g. poison.
  • While currently, space is a 2-D, in the future it should allow supporting different types of spaces.
  • Easily controlling biological aspects of physics such as mating rule, evolution, and intelligence inheritance.
  • It should allow extending the creature's capabilities, such as adding vocal communication and even love, hate, and motivation.
  • It should allow defining dynamic natures, like periods of dearth and epidemics.
  • Use dynamic graph deep learning framework such as TensorFlow 2.0 or Pytorch.
  • Use openAI baselines and/or Google dopamine projects as an implementation of the creature brain.
  • Using Unity or other engines, implement a visual simulation environment.
  • Add morphology and form to creatures that define its biological and physical features.

Far-reaching plans and directions

  • Model selection and hyper-parameter tuning. Show that Convolutional can better survive than fully connected layers.
  • Special relation between individuals based on graph networks to process the relationship between individuals.
  • Hebbian Learning vs. Gradient-based in a survival environment. Which is better?
  • Develop a game platform which allow training of a race and then allow it compete over resources with different race.
  • compare between the different implementation of RL algorithms, model-based and model-free, policy gradient and TD learning.