breakout-turbo-env 0.3.0

Deterministic, fixed-point, high-throughput Breakout vector environment

🕹️ Blazing-fast, deterministic Breakout for Reinforcement Learning 🕹️

breakout-turbo-env is a Python library for reinforcement-learning researchers and engineers who need many reproducible Breakout games behind one Gymnasium vector-environment API. Install it from PyPI, create BreakoutVecEnv, and step every lane with one NumPy action batch.

Fixed-point Rust physics owns game state and parallel stepping. Python exposes manual reset, policy-ready observations, native rendering, exact snapshots, and side-effect-free action branching.

Install

Requires Python 3.11+ on Apple-silicon macOS 11+ or x86-64 Linux with glibc 2.28+.

pip install breakout-turbo-env

Install optional tools only when needed:

pip install "breakout-turbo-env[play]"   # interactive Pygame player
pip install "breakout-turbo-env[train]"  # local PPO training with PyTorch

To work from source, install uv and a Rust toolchain, then run:

git clone https://github.com/tsilva/breakout-turbo-env.git
cd breakout-turbo-env
uv sync --frozen --extra dev --extra play --extra train
make develop-release

Use

import numpy as np
from breakout_turbo_env import BreakoutVecEnv

env = BreakoutVecEnv(num_envs=4096, num_threads=8)
obs, infos = env.reset()
obs, rewards, terminated, truncated, infos = env.step(
    np.zeros(env.num_envs, dtype=np.uint8)
)

done = terminated | truncated
if done.any():
    obs, reset_infos = env.reset(options={"reset_mask": done})

env.close()

Importing the package also registers BreakoutTurbo-v0, so callers may use gymnasium.make_vec("BreakoutTurbo-v0", ...). The complete lifecycle, configuration, snapshot, and branching contract is in the environment documentation.

Commands

uv run --extra play breakout-turbo-env play       # open the player
uv run --extra play breakout-turbo-env play --uncapped
uv run breakout-turbo-env benchmark               # benchmark the policy path
uv run python scripts/compare_stable_retro.py     # run live differential checks
uv run ruff check .                               # lint Python
uv run pytest -m "not stable_retro"               # run regular Python tests
cargo test --lib                                  # run Rust tests
make test-stable-retro                            # require live cartridge parity
uv run python train.py jerk                       # train a deterministic action tape
uv run --extra train python train.py ppo          # train a PPO policy
uv run --extra play python play.py jerk           # replay the newest JERK policy
uv run --extra play python play.py ppo            # replay the newest PPO policy

Append --help to the player, benchmark, training, or replay command for its options.

Notes

• Native actions are 0 noop, 1 FIRE, 2 right, and 3 left. The default policy observation is grayscale uint8, CHW, and shaped (num_envs, 4, 84, 84).
• Rewards are score deltas using Atari row scoring. There is no life-loss or board-clear shaping, and clearing the bricks does not terminate an episode.
• Autoreset is disabled. Reset terminated lanes explicitly with a Boolean reset_mask; unselected lanes remain byte-exact.
• The full start targets Stable Retro's native 160×210 Atari Breakout frame, lifecycle, physics, raster, rewards, and collision behavior. render() returns that RGB frame separately from policy observations.
• Live validation requires a separately obtained lawful ROM and a sibling stable-retro-turbo checkout. No ROM, save state, or recorded reference frame is distributed by this project.
• Only Apple-silicon macOS and x86-64 Linux are supported. See support, benchmarking, and release validation for exact boundaries.
• The project is a 0.x community preview. Public changes are recorded in the changelog; snapshots are portable only within the same package version and compatible configuration.

Architecture

License

MIT. See third-party notices for Atari, Stable Retro, ROM, and trademark boundaries.