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Next we discuss RL core elements, including value function, policy,\nreward, model, exploration vs. exploitation, and representation. Then we\ndiscuss important mechanisms for RL, including attention and memory,\nunsupervised learning, hierarchical RL, multi-agent RL, relational RL, and\nlearning to learn. After that, we discuss RL applications, including games,\nrobotics, natural language processing (NLP), computer vision, finance, business\nmanagement, healthcare, education, energy, transportation, computer systems,\nand, science, engineering, and art. Finally we summarize briefly, discuss\nchallenges and opportunities, and close with an epilogue.","url_abs":"http://arxiv.org/abs/1810.06339v1","url_pdf":"http://arxiv.org/pdf/1810.06339v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-reinforcement-learning","repo_url":"https://github.com/deepmind/lab","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"deep-reinforcement-learning","repo_url":"https://github.com/google/dopamine","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"deep-reinforcement-learning","repo_url":"https://github.com/mrkulk/hierarchical-deep-RL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"deep-reinforcement-learning","repo_url":"https://github.com/rllab/rllab","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"deep-reinforcement-learning","repo_url":"https://github.com/ayucd/why-would-you-do-that","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"management","task_name":"Management"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.06339","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.06339"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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