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# FC Portugal Codebase <br> for RoboCup 3D Soccer Simulation League
![](https://s5.gifyu.com/images/Siov6.gif)
## About
The FC Portugal Codebase was mainly written in Python, with some C++ modules. It was created to simplify and speed up the development of a team for participating in the RoboCup 3D Soccer Simulation League. We hope this release helps existing teams transition to Python more easily, and provides new teams with a robust and modern foundation upon which they can build new features.
## Documentation
The documentation is available [here](https://docs.google.com/document/d/1aJhwK2iJtU-ri_2JOB8iYvxzbPskJ8kbk_4rb3IK3yc/edit)
## Features
- The team is ready to play!
- Sample Agent - the active agent attempts to score with a kick, while the others maintain a basic formation
- Launch team with: **start.sh**
- Sample Agent supports [Fat Proxy](https://github.com/magmaOffenburg/magmaFatProxy)
- Launch team with: **start_fat_proxy.sh**
- Sample Agent Penalty - a striker performs a basic kick and a goalkeeper dives to defend
- Launch team with: **start_penalty.sh**
- Skills
- Get Ups (latest version)
- Walk (latest version)
- Dribble v1 (version used in RoboCup 2022)
- Step (skill-set-primitive used by Walk and Dribble)
- Basic kick
- Basic goalkeeper dive
- Features
- Accurate localization based on probabilistic 6D pose estimation [algorithm](https://doi.org/10.1007/s10846-021-01385-3) and IMU
- Automatic head orientation
- Automatic communication with teammates to share location of all visible players and ball
- Basics: common math ops, server communication, RoboViz drawings (with arrows and preset colors)
- Behavior manager that internally resets skills when necessary
- Bundle script to generate a binary and the corresponding start/kill scripts
- C++ modules are automatically built into shared libraries when changes are detected
- Central arguments specification for all scripts
- Custom A* pathfinding implementation in C++, optimized for the soccer environment
- Easy integration of neural-network-based behaviors
- Integration with Open AI Gym to train models with reinforcement learning
- User interface to train, retrain, test & export trained models
- Common features from Stable Baselines were automated, added evaluation graphs in the terminal
- Interactive FPS control during model testing, along with logging of statistics
- Interactive demonstrations, tests and utilities showcasing key features of the team/agents
- Inverse Kinematics
- Multiple agents can be launched on a single thread, or one agent per thread
- Predictor for rolling ball position and velocity
- Relative/absolute position & orientation of every body part & joint through forward kinematics and vision
- Sample train environments
- User-friendly interface to check active arguments and launch utilities & gyms
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