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BlockSeq

Code for 'Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning' (AAAI 2022, Oral presentation)

Install / Use

/learn @Giseung-Park/BlockSeq
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

Blockwise Sequential Model Learning

High_level_view

Code for 'Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning' (AAAI 2022, Oral presentation)

For installation and execution, please refer to the 'code_off_policy/Readme_Installation_Execution.txt' and 'code_on_policy_minigrid/Minigrid_Installation_Execution.txt.'

If there is any question or suggestion, don't hesitate to contact the author via gs.park@kaist.ac.kr.

Citation

@article{DBLP:journals/corr/abs-2112-05343,
  author    = {Giseung Park and
               Sungho Choi and
               Youngchul Sung},
  title     = {Blockwise Sequential Model Learning for Partially Observable Reinforcement
               Learning},
  journal   = {CoRR},
  volume    = {abs/2112.05343},
  year      = {2021},
  url       = {https://arxiv.org/abs/2112.05343},
  eprinttype = {arXiv},
  eprint    = {2112.05343},
  timestamp = {Tue, 14 Dec 2021 14:21:31 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2112-05343.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Related Skills

View on GitHub
GitHub Stars7
CategoryEducation
Updated1y ago
Forks1

Languages

Python

Security Score

70/100

Audited on Aug 8, 2024

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