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Analyzing Action Masking in the MiniHack Reinforcement Learning Environment

Cannon, Ian
http://orcid.org/0000-0002-4993-4600
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1671013536765918

Abstract Details

2022, Master of Computer Science (M.C.S.), University of Dayton, Computer Science.
Reinforcement Learning (RL) is an area of machine learning that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. NetHack presents a challenging problem for RL. It has a very large action space and multimodal observation space while requiring an agent to be capable of planning hundreds of thousands of timesteps to achieve a difficult goal. MiniHack is presented by Facebook AI Research to provide a testbed to develop incremental solutions toward the monumental goal of completing an ascension in NetHack. It presents a powerful framework for designing RL environments in procedurally generated worlds. Toward success in MiniHack, this thesis describes a method for masking actions to reduce the action space of agents. This thesis shows that masking actions can provide an effective means to artificially reduce the action space of any agent. Reducing the action space has been shown to increase the sample efficiency of agents in environments with large action spaces to few relevant actions.
Tam Nguyen (Advisor)
Zhongmei Yao (Committee Member)
James Buckley (Committee Member)
56 p.
Computer Engineering; Computer Science
reinforcement learning; action masking; rl; environment; minihack; nethack; ppo; neural networks; policy; action augmentation; action space; meta; facebook; research; exploration; game; games; game design; multimodal observations

Recommended Citations

Citations

  • Cannon, I. (2022). Analyzing Action Masking in the MiniHack Reinforcement Learning Environment [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1671013536765918

    APA Style (7th edition)

  • Cannon, Ian. Analyzing Action Masking in the MiniHack Reinforcement Learning Environment. 2022. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1671013536765918.

    MLA Style (8th edition)

  • Cannon, Ian. "Analyzing Action Masking in the MiniHack Reinforcement Learning Environment." Master's thesis, University of Dayton, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1671013536765918

    Chicago Manual of Style (17th edition)

dayton1671013536765918
36
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