ahu-dts307tc-toolkit 0.1.0

Shared utilities for DTS307TC reinforcement learning coursework.

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:

1. bump version in pyproject.toml and src/ahu_dts307tc_toolkit/__init__.py
2. commit and push
3. create a tag like v0.1.0
4. push the tag
5. GitHub Actions publishes the build to PyPI

The publishing workflow is defined in .github/workflows/publish.yml.