  The RoboCup 3D Soccer Simulation League serves as a competitive platform for
showcasing innovation in autonomous humanoid robot agents through simulated
soccer matches. Our team, FC Portugal, developed a new codebase from scratch in
Python after RoboCup 2021. The team's performance is based on a set of skills
centered around novel unifying primitives and a custom, symmetry-extended
version of the Proximal Policy Optimization algorithm. Our methods have been
thoroughly tested in official RoboCup matches, where FC Portugal has won the
last two main competitions, in 2022 and 2023. This paper presents our training
framework, as well as a timeline of skills developed using our
skill-set-primitives, which considerably improve the sample efficiency and
stability of skills, and motivate seamless transitions. We start with a
significantly fast sprint-kick developed in 2021 and progress to the most
recent skill set, which includes a multi-purpose omnidirectional walk, a
dribble with unprecedented ball control, a solid kick, and a push skill. The
push tackles both low-level collision-prone scenarios and high-level strategies
to increase ball possession. We address the resource-intensive nature of this
task through an innovative multi-agent learning approach. Finally, we release
the codebase of our team to the RoboCup community, enabling other teams to
transition to Python more easily and providing new teams with a robust and
modern foundation upon which they can build new features.
