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README.md

3D Balance Ball

In this projects we'll implementing agents that learns to play Unity 3D Balance Ball using several Deep Rl algorithms. Unity Ml Agents is a toolkit for developing and comparing reinforcement learning algorithms. We'll be using pytorch library for the implementation.

Libraries Used

  • Unity Ml Agents
  • PyTorch
  • numpy
  • matplotlib

About Enviroment

  • Set-up: A balance-ball task, where the agent balances the ball on it's head.
  • Goal: The agent must balance the ball on it's head for as long as possible.
  • Agents: The environment contains 12 agents of the same kind, all using the same Behavior Parameters.
  • Agent Reward Function:
    1. +0.1 for every step the ball remains on it's head.
    2. -1.0 if the ball falls off.
  • Behavior Parameters:
    1. Vector Observation space: 8 variables corresponding to rotation of the agent cube, and position and velocity of ball.
    2. Vector Observation space (Hard Version): 5 variables corresponding to rotation of the agent cube and position of ball.
    3. Vector Action space: (Continuous) Size of 2, with one value corresponding to X-rotation, and the other to Z-rotation.
    4. Visual Observations: None.
  • Float Properties: Three
    1. scale: Specifies the scale of the ball in the 3 dimensions (equal across the three dimensions)
      • Default: 1
      • Recommended Minimum: 0.2
      • Recommended Maximum: 5
    2. gravity: Magnitude of gravity
      • Default: 9.81
      • Recommended Minimum: 4
      • Recommended Maximum: 105
    3. mass: Specifies mass of the ball
      • Default: 1
      • Recommended Minimum: 0.1
      • Recommended Maximum: 20
  • Benchmark Mean Reward: 100

Deep RL Agents

Any questions

If you have any questions, feel free to ask me:

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How to help

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  • Improve our notebooks: if you found a bug or a better implementation you can send a pull request.