{"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/vizdoom-competitions-playing-doom-from-pixels","title":"ViZDoom Competitions: Playing Doom from Pixels","arxiv_id":"1809.03470","date":"2018-09-10","proceeding":null,"authors":["Marek Wydmuch","Michał Kempka","Wojciech Jaśkowski"],"abstract":"This paper presents the first two editions of Visual Doom AI Competition,\nheld in 2016 and 2017. The challenge was to create bots that compete in a\nmulti-player deathmatch in a first-person shooter (FPS) game, Doom. The bots\nhad to make their decisions based solely on visual information, i.e., a raw\nscreen buffer. To play well, the bots needed to understand their surroundings,\nnavigate, explore, and handle the opponents at the same time. These aspects,\ntogether with the competitive multi-agent aspect of the game, make the\ncompetition a unique platform for evaluating the state of the art reinforcement\nlearning algorithms. The paper discusses the rules, solutions, results, and\nstatistics that give insight into the agents' behaviors. Best-performing agents\nare described in more detail. The results of the competition lead to the\nconclusion that, although reinforcement learning can produce capable Doom bots,\nthey still are not yet able to successfully compete against humans in this\ngame. The paper also revisits the ViZDoom environment, which is a flexible,\neasy to use, and efficient 3D platform for research for vision-based\nreinforcement learning, based on a well-recognized first-person perspective\ngame Doom.","url_abs":"http://arxiv.org/abs/1809.03470v1","url_pdf":"http://arxiv.org/pdf/1809.03470v1.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":"vizdoom-competitions-playing-doom-from-pixels","repo_url":"https://github.com/mwydmuch/ViZDoom","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"vizdoom-competitions-playing-doom-from-pixels","repo_url":"https://github.com/farama-foundation/vizdoom","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"vizdoom-competitions-playing-doom-from-pixels","repo_url":"https://github.com/hegde95/ViZDoom_with_Sound","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"vizdoom-competitions-playing-doom-from-pixels","repo_url":"https://github.com/icmlanon58443043/vizdoom","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"vizdoom-competitions-playing-doom-from-pixels","repo_url":"https://github.com/icmlanon58443043/vizdoomicmlanon","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"vizdoom-competitions-playing-doom-from-pixels","repo_url":"https://github.com/nolanwinsman/Team-Doom","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"vizdoom-competitions-playing-doom-from-pixels","repo_url":"https://github.com/sagpant/ViZDoom","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"navigate","task_name":"Navigate"},{"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=1809.03470","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.03470"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/hegde95/ViZDoom_with_Sound","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/icmlanon58443043/vizdoom","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/mwydmuch/ViZDoom","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/icmlanon58443043/vizdoomicmlanon","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/nolanwinsman/Team-Doom","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/sagpant/ViZDoom","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/farama-foundation/vizdoom","reach":{"status":"ok"}}],"summary":{"unverified":2},"by_repo_kind":{"listed":{"samples":2,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":2,"samples":[{"code_sha256_prefix":"a2c26261ca60dbd2","entry":"get_q_values","repo":"hegde95/ViZDoom_with_Sound","repo_kind":"listed","path":"examples/python/learning_pytorch.py","file_url":"https://github.com/hegde95/ViZDoom_with_Sound/blob/HEAD/examples/python/learning_pytorch.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"a2c26261ca60dbd2"}},{"code_sha256_prefix":"c49e869676fa9a5f","entry":"learn","repo":"hegde95/ViZDoom_with_Sound","repo_kind":"listed","path":"examples/python/learning_pytorch.py","file_url":"https://github.com/hegde95/ViZDoom_with_Sound/blob/HEAD/examples/python/learning_pytorch.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"c49e869676fa9a5f"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}