{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/beating-atari-with-natural-language-guided","title":"Beating Atari with Natural Language Guided Reinforcement Learning","arxiv_id":"1704.05539","date":"2017-04-18","proceeding":null,"authors":["Russell Kaplan","Christopher Sauer","Alexander Sosa"],"abstract":"We introduce the first deep reinforcement learning agent that learns to beat\nAtari games with the aid of natural language instructions. The agent uses a\nmultimodal embedding between environment observations and natural language to\nself-monitor progress through a list of English instructions, granting itself\nreward for completing instructions in addition to increasing the game score.\nOur agent significantly outperforms Deep Q-Networks (DQNs), Asynchronous\nAdvantage Actor-Critic (A3C) agents, and the best agents posted to OpenAI Gym\non what is often considered the hardest Atari 2600 environment: Montezuma's\nRevenge.","url_abs":"http://arxiv.org/abs/1704.05539v1","url_pdf":"http://arxiv.org/pdf/1704.05539v1.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":"beating-atari-with-natural-language-guided","repo_url":"https://github.com/deniztekalp/comp-491-bitirme","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"atari-games","task_name":"Atari Games"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"montezumas-revenge","task_name":"Montezuma's Revenge"},{"task_slug":"openai-gym","task_name":"OpenAI Gym"},{"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=1704.05539","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}