{"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/multi-agent-generative-adversarial-imitation","title":"Multi-Agent Generative Adversarial Imitation Learning","arxiv_id":"1807.09936","date":"2018-07-26","proceeding":"NeurIPS 2018 12","authors":["Jiaming Song","Hongyu Ren","Dorsa Sadigh","Stefano Ermon"],"abstract":"Imitation learning algorithms can be used to learn a policy from expert\ndemonstrations without access to a reward signal. However, most existing\napproaches are not applicable in multi-agent settings due to the existence of\nmultiple (Nash) equilibria and non-stationary environments. We propose a new\nframework for multi-agent imitation learning for general Markov games, where we\nbuild upon a generalized notion of inverse reinforcement learning. We further\nintroduce a practical multi-agent actor-critic algorithm with good empirical\nperformance. Our method can be used to imitate complex behaviors in\nhigh-dimensional environments with multiple cooperative or competing agents.","url_abs":"http://arxiv.org/abs/1807.09936v1","url_pdf":"http://arxiv.org/pdf/1807.09936v1.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":"multi-agent-generative-adversarial-imitation","repo_url":"https://github.com/ermongroup/multiagent-gail","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"imitation-learning","task_name":"Imitation Learning"},{"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":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1807.09936","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.09936"}},"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/ermongroup/multiagent-gail","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":1},"by_repo_kind":{"listed":{"samples":1,"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":0,"samples":[{"code_sha256_prefix":"4bc3c89aa4d71e84","entry":"make_output_format","repo":"ermongroup/multiagent-gail","repo_kind":"listed","path":"multiagent-gail/rl/logger.py","file_url":"https://github.com/ermongroup/multiagent-gail/blob/HEAD/multiagent-gail/rl/logger.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4bc3c89aa4d71e84"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}