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bizket

https://github.com/victorsevero/bizket

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.8%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: victorsevero
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: main
  • Size: 3.62 MB
Statistics
  • Stars: 10
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 3 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License Citation

README.md

DOI

Reproducible Reinforcement Learning for Mega Man X4

How to reproduce

  1. Extract ROM image from your own Mega Man X4 (US version) original PSX disk
  2. Extract PSX BIOS from your own console
  3. Download BizHawk 2.9-rc2 from here and extract it locally
  4. Copy states\eregion.State to your BizHawk BizHawk-rc2\PSX\State directory and rename it to Mega Man X4 (USA).Nymashock.QuickSave0.State
  5. Create a virtual environment and install dependencies from pyproject.toml with Poetry or install dependencies with pip from requirements.txt
  6. Create a copy of models_configs\example.yml inside the same directory and rename it to sevs.yml; fill in all the necessary parameters inside the paths level. Adjust any other parameters if you like.
  7. Run main.py.
  8. Results will show up during runtime inside logs directory. You can visualize them with TensorBoard by running tensorboard --logdir logs inside project root directory.

PS.: Windows only, sorry! The connection method depends on Windows-exclusive features of the emulator.

PS².: These instructions reproduce only the first boss of the game playing with the character named Zero. ~~I will make a more detailed guide in the future when I turn this into a library.~~ UPDATE: In the future, I will adapt this case for stable-retro, which is the most promising successor of OpenAI's retro library and which I contribute to.

Explanatory Video in Portuguese

https://www.youtube.com/watch?v=zVA7WZxvtyA

Final RL models beating all main bosses of the game

https://www.youtube.com/watch?v=uYRemfDmwTk

Owner

  • Name: Victor Severo
  • Login: victorsevero
  • Kind: user
  • Location: São Paulo
  • Company: @grupo-sbf

Data Scientist at @grupo-sbf

Citation (CITATION.cff) 

cff-version: 1.0.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "SEVERO"
  given-names: "VICTOR"
  orcid: "https://orcid.org/0009-0001-2657-9589"
title: "Reproducible Reinforcement Learning for Mega Man X4"
version: 1.0.0
doi: 10.5281/zenodo.11238646
date-released: 2024-05-21
url: "https://github.com/victorsevero/bizket"

GitHub Events

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Last Year

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 72
  • Total Committers: 1
  • Avg Commits per committer: 72.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Victor Severo v****a@g****m 72

Issues and Pull Requests

Last synced: 9 months ago

All Time
  • Total issues: 0
  • Total pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: 5 minutes
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
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  • victorsevero (1)
Top Labels
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Dependencies

poetry.lock pypi
  • 163 dependencies
requirements.txt pypi
  • absl-py ==1.3.0
  • ale-py ==0.8.0
  • alembic ==1.8.1
  • attrs ==22.1.0
  • autopage ==0.5.1
  • autorom ==0.4.2
  • autorom-accept-rom-license ==0.4.2
  • cachetools ==5.2.0
  • certifi ==2022.9.24
  • charset-normalizer ==2.1.1
  • click ==8.1.3
  • cliff ==4.1.0
  • cloudpickle ==2.2.0
  • cmaes ==0.9.0
  • cmd2 ==2.4.2
  • colorama ==0.4.6
  • colorlog ==6.7.0
  • commonmark ==0.9.1
  • contourpy ==1.0.6
  • cycler ==0.11.0
  • fonttools ==4.38.0
  • google-auth ==2.14.1
  • google-auth-oauthlib ==0.4.6
  • graphviz ==0.20.1
  • greenlet ==2.0.1
  • grpcio ==1.50.0
  • gym ==0.26.2
  • gym-notices ==0.0.8
  • idna ==3.4
  • importlib-metadata ==4.13.0
  • importlib-resources ==5.10.0
  • kiwisolver ==1.4.4
  • mako ==1.2.4
  • markdown ==3.4.1
  • markupsafe ==2.1.1
  • matplotlib ==3.6.2
  • numpy ==1.23.5
  • oauthlib ==3.2.2
  • opencv-python ==4.6.0.66
  • optuna ==3.0.3
  • packaging ==21.3
  • pandas ==1.5.2
  • pbr ==5.11.0
  • pillow ==9.3.0
  • prettytable ==3.5.0
  • protobuf ==3.20.3
  • psutil ==5.9.4
  • pyasn1 ==0.4.8
  • pyasn1-modules ==0.2.8
  • pygame ==2.1.2
  • pygetwindow ==0.0.9
  • pygments ==2.13.0
  • pyparsing ==3.0.9
  • pyperclip ==1.8.2
  • pyreadline3 ==3.4.1
  • pyrect ==0.2.0
  • python-dateutil ==2.8.2
  • pytz ==2022.6
  • pyyaml ==6.0
  • requests ==2.28.1
  • requests-oauthlib ==1.3.1
  • rich ==12.6.0
  • rsa ==4.9
  • scipy ==1.8.1
  • setuptools ==65.6.3
  • setuptools-scm ==7.0.5
  • six ==1.16.0
  • sqlalchemy ==1.4.44
  • stevedore ==4.1.1
  • tensorboard ==2.11.0
  • tensorboard-data-server ==0.6.1
  • tensorboard-plugin-wit ==1.8.1
  • tomli ==2.0.1
  • torch-tb-profiler ==0.4.0
  • torchviz ==0.0.2
  • tqdm ==4.64.1
  • typing-extensions ==4.4.0
  • urllib3 ==1.26.13
  • wcwidth ==0.2.5
  • werkzeug ==2.2.2
  • wheel ==0.38.4
  • zipp ==3.11.0
pyproject.toml pypi