GitHub - desi-ivanov/cars-rl: Reinforcement Learning (DQN) from scratch with zero dependencies applied to a simple car driving game

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Reinforcement Learning (DQN) from scratch with zero dependencies applied to a simple car driving game

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MIT license

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cars-rl

Reinforcement Leanring (DQN) from scratch with zero dependencies applied to a simple car driving game. Check it out here!

Untitled.mp4

Details

The agent has 3 inputs which define the vicinity of walls to the sides of the car, each in the range of [0, 1]. At each step the reward is:

  • 1 if the car reaches a new point on the map (the map is divided into a grid of 10x10 cells)
  • 0.01 if the car does not crash
  • -10 if the car crashes

References

This project is heavily based on:

License

MIT

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Reinforcement Learning (DQN) from scratch with zero dependencies applied to a simple car driving game

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