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Walker

In this projects we'll implementing agents that learns to play Unity Walker 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: Physics-based Humanoids agents with 26 degrees of freedom. These DOFs correspond to articulation of the following body-parts: hips, chest, spine, head, thighs, shins, feet, arms, forearms and hands.
  • Goal: The agents must move its body toward the goal direction as quickly as possible without falling.
  • Agents: The environment contains 11 independent agents with same Behavior Parameters.
  • Agent Reward Function (independent):
    • +0.03 times body velocity in the goal direction.
    • +0.01 times head y position.
    • +0.01 times body direction alignment with goal direction.
    • -0.01 times head velocity difference from body velocity.
  • Behavior Parameters:
    • Vector Observation space: 215 variables corresponding to position, rotation, velocity, and angular velocities of each limb, along with goal direction.
    • Vector Action space: (Continuous) Size of 39, corresponding to target rotations applicable to the joints.
    • Visual Observations: None
  • Float Properties: Four
    • gravity: Magnitude of gravity
      • Default: 9.81
      • Recommended Minimum:
      • Recommended Maximum:
    • hip_mass: Mass of the hip component of the walker
      • Default: 15
      • Recommended Minimum: 7
      • Recommended Maximum: 28
    • chest_mass: Mass of the chest component of the walker
      • Default: 8
      • Recommended Minimum: 3
      • Recommended Maximum: 20
    • spine_mass: Mass of the spine component of the walker
      • Default: 10
      • Recommended Minimum: 3
      • Recommended Maximum: 20
  • Benchmark Mean Reward: 1000

Deep RL Agents

Any questions

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

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How to help

  • Clap on articles: Clapping in Medium means that you really like my articles. And the more claps I have, the more my article is shared help them to be much more visible to the deep learning community.
  • Improve our notebooks: if you found a bug or a better implementation you can send a pull request.