haxballgym
A composable, batched, RLGym-v2-style reinforcement-learning environment for
Haxball, built on top of the headless haxball_core
physics engine.
The physics is a faithful Rust port of Haxball's, verified to agree with the original engine to within 1e-9, and stepped in batches of thousands of matches in parallel — fast enough for serious self-play on a single machine.
Install
pip install haxballgym
This pulls in haxball_core (the Rust engine) automatically.
Quick start
from haxballgym import make_default_env
env = make_default_env(n_envs=512)
obs = env.reset()
obs, rewards, terminated, truncated = env.step(actions)
Or compose your own environment from the pieces — engine plus obs / action / reward / done / state-mutator builders:
from haxballgym import (
TransitionEngine, DefaultObs, DiscreteAction,
CombinedReward, GoalReward, TouchReward,
GoalCondition, TimeoutCondition, KickoffMutator, Env,
)
See the project repository for the full docs, runnable training examples, and tools to play your trained policy back in the real browser game.
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for haxballgym 1.0.1
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