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README.md

Reacher

In this projects we'll implementing agents that learns to play Unity Reacher 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: Double-jointed arm which can move to target locations.
  • Goal: The agents must move its hand to the goal location, and keep it there.
  • Agents: The environment contains 10 agent with same Behavior Parameters.
  • Agent Reward Function (independent):
    • +0.1 Each step agent's hand is in goal location.
  • Behavior Parameters:
    • Vector Observation space: 26 variables corresponding to position, rotation, velocity, and angular velocities of the two arm Rigidbodies.
    • Vector Action space: (Continuous) Size of 4, corresponding to torque applicable to two joints.
    • Visual Observations: None.
  • Float Properties: Five
    • goal_size: radius of the goal zone
      • Default: 5
      • Recommended Minimum: 1
      • Recommended Maximum: 10
    • goal_speed: speed of the goal zone around the arm (in radians)
      • Default: 1
      • Recommended Minimum: 0.2
      • Recommended Maximum: 4
    • gravity
      • Default: 9.81
      • Recommended Minimum: 4
      • Recommended Maximum: 20
    • deviation: Magnitude of sinusoidal (cosine) deviation of the goal along the vertical dimension
      • Default: 0
      • Recommended Minimum: 0
      • Recommended Maximum: 5
    • deviation_freq: Frequency of the cosine deviation of the goal along the vertical dimension
      • Default: 0
      • Recommended Minimum: 0
      • Recommended Maximum: 3
  • Benchmark Mean Reward: 30

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.