A Soccer-Twos game variant. Built to work as a reinforcement learning environment with this package.
Features:
- Added a "watching" mode with better visualization and cool animations;
- Added position & velocity setting communication channels (for e.g. curriculum learning);
- Simplified integration with Python
This environment is based on Unity ML Agents' Soccer Twos, so most of the specs are the same. Here, four agents compete in a 2 vs 2 toy soccer game, aiming to get the ball into the opponent's goal while preventing the ball from entering own goal.
- Observation space: a 336-dimensional vector corresponding to 11 ray-casts forward distributed over 120 degrees 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 over three observation stacks.
- Action space: 3 discrete branched actions (MultiDiscrete) corresponding to forward, backward, sideways movement, as well as rotation (27 discrete actions).
- Agent Reward Function:
1 - accumulated time penalty: when ball enters opponent's goal. Accumulated time penalty is incremented by(1 / MaxSteps)every fixed update and is reset to 0 at the beginning of an episode. In this build,MaxSteps = 5000.-1: when ball enters team's goal.