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[P] Ball & beam gym – control theory lab simulations as Open AI gym environments

While trying out reinforcement learning I built some custom ball & beam environments since I was already familiar with it from control theory labs. I built it as a first order system where the angle of the beam is under full control (did not want to spend time simulating a motor). So it can be baselined by using a simple PID controller.

https://github.com/simon-larsson/ballbeam-gym

There are currently environments for three objectives:

  • Balancing – just keeping the ball on beam
  • Setpoint – keeping the ball as close as possible to a setpoint
  • Throw – throwing the ball as far as possible to the right

The environments have two different state spaces. The agent can either use key-variables (position, velocity, angle) or the images from the visualization as state space.

Hope someone else wants to try it! 🙂

submitted by /u/lilsmacky
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.