ahu-dts307tc-toolkit

PyPI

Shared utilities for DTS307TC reinforcement learning coursework.

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Direct dependencies12From release 0.1.0

Last observed21 Oct, 09:44 UTC

README

Version 0.1.0 documentation

Shared building blocks that are reusable across DTS307TC Coursework 1 and Coursework 2.

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.

From PyPI:

pip install ahu-dts307tc-toolkit

For local development:

uv sync --extra dev

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

This package is prepared for PyPI Trusted Publishing through GitHub Actions.

Release flow:

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

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Version	Published	Direct dependencies	Open version
0.1.0	28 Mar 2026	12

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package:7894b23cdd1865b173f3be9e2c03ab52271e72c91bac90c026337b0ad1bcc66a