breakout-turbo-env 0.3.2

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 registers the Stable Retro-compatible Breakout-Atari2600-v0 environment. BreakoutTurbo-v0 remains available as a legacy native-action alias. 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. The cartridge presents two walls: the first refills after the next paddle return, the second ends at score 864 without another refill, and only losing all five lives terminates the episode.
• Autoreset is disabled. Reset terminated lanes explicitly with a Boolean reset_mask; unselected lanes remain byte-exact.
• The canonical Start state targets Stable Retro's native 160×210 Atari Breakout frame, lifecycle, physics, raster, rewards, collision behavior, and public trajectory values. In particular, ball_y uses the Atari RAM convention where zero means the serve is waiting for FIRE. render() returns the RGB frame separately from policy observations. The legacy start name full aliases Start.
• 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.