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Image Processing Project

Feature-Based Image Processing and Reinforcement Learning for CarRacing-v3

Joe / Student ID 1103820

An inspectable image-processing pipeline converts CarRacing-v3 RGB frames into compact road-geometry features for PPO/SAC control evaluation. Instead of training a raw-pixel CNN policy, the package emphasizes an explicit, inspectable, and lightweight visual state representation.

Demo

Best PPO demo video preview

PPO and SAC demo videos
Open the demo page to view pauseable PPO and SAC preview clips with conservative result notes.
Open PPO + SAC demo page · Full video gallery

Key Results

Branch Status Validated eval Best parsed eval
PPO completed baseline Completed 500K baseline 938.87 +/- 7.86 @ 500,000 939.53 +/- 4.09 @ 480,000
SAC fast-result branch Partial 400K best-checkpoint / fast-result branch 938.51 +/- 4.88 @ 400,000 938.51 +/- 4.88 @ 400,000

Conservative interpretation. PPO is a fully completed 500K baseline. SAC is a strong but partial fast-result branch whose validated number is the 400K checkpoint. The PPO/SAC comparison is not compute-equivalent, and SAC is not presented as outperforming PPO. The project makes no environment-completion claim.

Metric context. In CarRacing-v3, reward is tied to track-tile coverage with a per-frame time penalty, so scores in the 930s should be read as high-return evaluation episodes under the saved protocol. This package still does not make a general environment-completion or compute-equivalent PPO/SAC claim.

Method at a glance

Feature-based visual-control pipeline: frame, perception, geometry, state vector, temporal context, and control

  • RGB frame from Gymnasium CarRacing-v3.
  • HSV road mask and compact road-geometry extraction.
  • Ray/radar features summarized into a 16D base observation.
  • Four-frame temporal stack produces a 64D policy input.
  • Standard Stable-Baselines3 PPO/SAC MLP policies evaluate the representation.

This project package does not include raw-pixel CNN policy training.

Project Materials

Material Link
Final IEEE-format technical report PDF 1103820_Joe_Image_Processing_Project_V2_IEEE-format report.pdf
Overleaf / LaTeX source package final/overleaf/Image_Processing_Project_Overleaf_Package.zip
Final presentation (PDF) final/slides/Image_Processing_Project_Final_Presentation.pdf
Final presentation (PPTX) final/slides/Image_Processing_Project_Final_Presentation.pptx
PPO notebook final/notebooks/Final_PPO_Baseline_CarRacing_v3.ipynb
SAC notebook final/notebooks/Final_SAC_Fast_Result_CarRacing_v3.ipynb
Result summary final/docs/RESULT_SUMMARY.md
Figures final/figures/
Video gallery final/videos/README.md

Repository Structure

final/                 public-facing final package
  report/              final IEEE-format report PDF
  overleaf/            LaTeX source package for the report
  slides/              final presentation (PPTX / PDF)
  notebooks/           PPO and SAC notebooks with saved outputs
  figures/             report and slide figures + demo poster
  tables/              CSV result evidence
  videos/              demo MP4s, previews, manifest, gallery README
  logs/                training and evaluation logs
  docs/                result summary, run instructions, dataset notes
  validation/          static validation report
archive/               preserved history, not the current project package

How to review / reproduce

Start with final/README.md, the result summary, and the Final IEEE-format technical report PDF. The notebooks are kept in safe report/evaluation mode and include saved output evidence; they do not require retraining to inspect. Do not rerun training unless you intentionally change the project scope. For local/static inspection guidance and Colab-safe report/evaluation mode, see final/docs/RUN_INSTRUCTIONS.md.

Key evidence paths:

Limitations

  • The SAC fast-result branch is a partial 400K checkpoint, not a full-length 500K run.
  • SAC is not presented as outperforming PPO.
  • PPO and SAC are not a compute-equivalent comparison.
  • This project package does not include raw-pixel CNN policy training.
  • The project makes no environment-completion claim for CarRacing-v3.
  • HSV-style visual preprocessing can be sensitive to rendering and track appearance changes.

Archived Research History

The original V1 PPO-only submission and the dated V2 build snapshot are preserved under archive/ for traceability. They are not the current public project package. V1 results must not be confused with the V2 PPO/SAC results above.

References / Related Tools

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Feature-based image processing and PPO/SAC ray-feature evaluation for Gymnasium CarRacing-v3.

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