Autonomous Self-Driving Car Simulation


  • Graphics creation using Kivy modules in Python. A car with 3 sensors in front used in the simulation.
  • Deep Q-Learning reinforcement techniques used with specific reward policies where the taxi runs downtown to the airport and back.
  • Sand used for its simulation so that the taxi can learn through experiences stored in a batch of 100.
  • Reward = -1 given to the taxi when it crashes into the sand or reaches outskirts of the city, Reward = -0.2 given when it moves further away from the destination, Reward = +0.1 given when it approaches in correct direction of the destination, Reward =+1 given when it reaches the goal.