Skip to main content

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

Project description

breakout-turbo-env logo

🕹️ 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.

Native Breakout gameplay rendered by breakout-turbo-env

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})

Interesting live positions can be archived without advancing the game and restored into any lane of the same environment:

capture_mask = np.zeros(env.num_envs, dtype=np.bool_)
capture_mask[0] = True
captured = env.capture_snapshots(capture_mask)

restore_mask = np.zeros(env.num_envs, dtype=np.bool_)
restore_mask[3] = True
starts = [None] * env.num_envs
starts[3] = captured[0]
obs, infos = env.reset(
    options={"reset_mask": restore_mask, "snapshots": starts},
)
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. Serialized get_state() snapshots are portable only within the same package version and compatible configuration; live snapshot handles are session-local and intentionally not pickleable.

Architecture

breakout-turbo-env architecture

License

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

breakout_turbo_env-0.3.3.tar.gz (10.3 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

breakout_turbo_env-0.3.3-cp311-abi3-manylinux_2_28_x86_64.whl (313.8 kB view details)

Uploaded CPython 3.11+manylinux: glibc 2.28+ x86-64

breakout_turbo_env-0.3.3-cp311-abi3-macosx_11_0_arm64.whl (282.2 kB view details)

Uploaded CPython 3.11+macOS 11.0+ ARM64

File details

Details for the file breakout_turbo_env-0.3.3.tar.gz.

File metadata

  • Download URL: breakout_turbo_env-0.3.3.tar.gz
  • Upload date:
  • Size: 10.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for breakout_turbo_env-0.3.3.tar.gz
Algorithm Hash digest
SHA256 fc6c6c547e429083b93adede74ff4b4481849ae79c3c5b334ddd5bd5dbd6c5e5
MD5 c54dab13992d7bcef9c88d950b1bb3d4
BLAKE2b-256 abf20d2cff7877c08bd1a3c3ccd7efc06d617a82d57e3cf0fdb3a67d4541a61f

See more details on using hashes here.

Provenance

The following attestation bundles were made for breakout_turbo_env-0.3.3.tar.gz:

Publisher: release.yml on tsilva/breakout-turbo-env

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file breakout_turbo_env-0.3.3-cp311-abi3-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for breakout_turbo_env-0.3.3-cp311-abi3-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 8793037192503bac83d8d997aede9ce52a9d813db07b3e0b392af888f16cbe94
MD5 29af5d44d9c26d2284249bf685f55e96
BLAKE2b-256 38352a15687d40c5603d8da5de2352d9b5196b5fa390a7ea7f50a11ab0a75053

See more details on using hashes here.

Provenance

The following attestation bundles were made for breakout_turbo_env-0.3.3-cp311-abi3-manylinux_2_28_x86_64.whl:

Publisher: release.yml on tsilva/breakout-turbo-env

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file breakout_turbo_env-0.3.3-cp311-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for breakout_turbo_env-0.3.3-cp311-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 a6c4ca22d05a922c2f1e69e39afec280397d85d72a8744fc9f63bd5478aa79a5
MD5 c5e465adab92c8878aee1f5d39fc5c38
BLAKE2b-256 c0ec0e4b16756a16109770c693792459e6de230b0a79b698ae0ee10cc98fc08b

See more details on using hashes here.

Provenance

The following attestation bundles were made for breakout_turbo_env-0.3.3-cp311-abi3-macosx_11_0_arm64.whl:

Publisher: release.yml on tsilva/breakout-turbo-env

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page