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README.md

Soccer Twos

In this projects we'll implementing agents that learns to play Unity Soccer Twos 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: Environment where four agents compete in a 2 vs 2 toy soccer game.
  • Goal:
    • Get the ball into the opponent's goal while preventing the ball from entering own goal.
    • Goalie:
  • Agents: The environment contains four agents, with the same Behavior Parameters : Soccer.
  • Agent Reward Function (dependent):
    • +1 When ball enters opponent's goal.
    • -1 When ball enters team's goal.
    • -0.001 Existential penalty.
  • Behavior Parameters:
    • Vector Observation space: 336 corresponding to 11 ray-casts forward distributed over 120 degrees (264) and 3 ray-casts backward distributed over 90 degrees each detecting 6 possible object types, along with the object's distance. The forward ray-casts contribute 264 state dimensions and backward 72 state dimensions.
    • Vector Action space: (Discrete) Three branched actions corresponding to forward, backward, sideways movement, as well as rotation.
    • Visual Observations: None
  • Float Properties: Two
    • ball_scale: Specifies the scale of the ball in the 3 dimensions (equal across the three dimensions)
      • Default: 7.5
      • Recommended minimum: 4
      • Recommended maximum: 10
    • gravity: Magnitude of the gravity
      • Default: 9.81
      • Recommended minimum: 6
      • Recommended maximum: 20

Deep RL Agents

Any questions

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