Reinforcement Learning in the MiniHack Learning Environment

Reinforcement Learning in the MiniHack Learning Environment

    Presenter(s)

    Ian M. Cannon

    Comments

    Presentation: 1:20 p.m.-1:40 p.m., Kennedy Union 211

    Description

    Reinforcement Learning is a branch of machine learning in which a computer agent receives rewards for interacting with its environment by being given observations, formulating actions, and taking those actions in the environment. Reinforcement Learning has been used to exceed human performance in game-like environments from Checkers to Go to DoTA2. Many of these achievements were hard-fought by overcoming challenges in each environment individually. NetHack is a challenging game that makes an excellent Reinforcement Learning environment for its combinations of broad action and observation space with sparse rewards and general difficulty. In this work, we investigate reinforcement learning methods in MiniHack which is a mini version of NetHack. We will introduce challenges of the difficult game of NetHack and explain how MiniHack can break up this large environment into smaller, composeable, more tractable mini-environments. We have used this environment to solve challenging problems in Reinforcement Learning and will talk through these challenges and what we have done to overcome them.

    Publication Date

    4-20-2022

    Project Designation

    Graduate Research

    Primary Advisor

    Van Tam Nguyen

    Primary Advisor's Department

    Computer Science

    Keywords

    Stander Symposium project, College of Arts and Sciences

    United Nations Sustainable Development Goals

    Good Health and Well-Being; Quality Education

    Reinforcement Learning in the MiniHack Learning Environment

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