ahu-dts307tc-toolkit
Shared building blocks that are reusable across DTS307TC Coursework 1 and Coursework 2.
Scope
This package only contains logic that can serve both coursework projects, such as:
- environment helpers and pixel preprocessing,
- shared neural-network building blocks,
- PPO and DQN data buffers,
- TensorBoard logging and scalar readers,
- plotting helpers,
- evaluation export utilities,
- video generation helpers,
- general utility functions such as checkpointing, config loading, device selection, and seeding.
It does not contain coursework-specific training orchestration or report logic.
Install
From PyPI:
pip install ahu-dts307tc-toolkit
For local development:
uv sync --extra dev
Package Layout
| Module | Contents |
|---|---|
env |
generic make_pixel_env, make_carracing_env, pixel wrappers, RecordEpisodeStats |
models |
NatureCNN, SmallCNN, EnhancedCNN, MLP blocks, policy/value and Q-network heads |
buffers |
RolloutBuffer for PPO and ReplayBuffer for DQN |
logging |
TensorBoard logger and metric helpers |
evaluation |
deterministic evaluation, TensorBoard scalar readers, CSV/JSON export |
viz |
plotting helpers for coursework experiments |
media |
video rendering and export helpers |
utils |
TOML loading, checkpoint I/O, seeding, device helpers |
Release Workflow
This package is prepared for PyPI Trusted Publishing through GitHub Actions.
Release flow:
- bump
versioninpyproject.tomlandsrc/ahu_dts307tc_toolkit/__init__.py - commit and push
- create a tag like
v0.1.0 - push the tag
- GitHub Actions publishes the build to PyPI
The publishing workflow is defined in .github/workflows/publish.yml.
1 maintainer
Metadata
1 maintainer
Release files for ahu-dts307tc-toolkit 0.1.0
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Total release size: 193.3 kB
Release files / ahu_dts307tc_toolkit-0.1.0.tar.gz
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