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We argue that, since an agent with Artificial General Intelligence (AGI) should have no problem engaging in tasks that inherently involve non-Archimedean rewards, and since traditional reinforcement learning rewards are real numbers, therefore traditional reinforcement learning probably will not lead to AGI. We indicate two possible ways traditional reinforcement learning could be altered to remove this roadblock.<\/jats:p>","DOI":"10.2478\/jagi-2020-0004","type":"journal-article","created":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T15:09:08Z","timestamp":1604675348000},"page":"70-85","source":"Crossref","is-referenced-by-count":3,"title":["The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI"],"prefix":"10.2478","volume":"11","author":[{"given":"Samuel Allen","family":"Alexander","sequence":"first","affiliation":[{"name":"Quantitative Research Analyst, The U.S. Securities and Exchange Commission, New York Regional Office"}]}],"member":"374","published-online":{"date-parts":[[2020,10,15]]},"reference":[{"key":"2026042814150099603_j_jagi-2020-0004_ref_001_w2aab3b7b6b1b6b1ab1ab1Aa","doi-asserted-by":"crossref","unstructured":"Al-Dhalimy, H., and Geyer, C. J. 2016. Surreal Time and Ultratasks. 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