{"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/discriminator-actor-critic-addressing-sample","title":"Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning","arxiv_id":"1809.02925","date":"2018-09-09","proceeding":"ICLR 2019 5","authors":["Ilya Kostrikov","Kumar Krishna Agrawal","Debidatta Dwibedi","Sergey Levine","Jonathan Tompson"],"abstract":"We identify two issues with the family of algorithms based on the Adversarial\nImitation Learning framework. The first problem is implicit bias present in the\nreward functions used in these algorithms. While these biases might work well\nfor some environments, they can also lead to sub-optimal behavior in others.\nSecondly, even though these algorithms can learn from few expert\ndemonstrations, they require a prohibitively large number of interactions with\nthe environment in order to imitate the expert for many real-world\napplications. In order to address these issues, we propose a new algorithm\ncalled Discriminator-Actor-Critic that uses off-policy Reinforcement Learning\nto reduce policy-environment interaction sample complexity by an average factor\nof 10. Furthermore, since our reward function is designed to be unbiased, we\ncan apply our algorithm to many problems without making any task-specific\nadjustments.","url_abs":"http://arxiv.org/abs/1809.02925v2","url_pdf":"http://arxiv.org/pdf/1809.02925v2.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":"discriminator-actor-critic-addressing-sample","repo_url":"https://github.com/Kaixhin/imitation-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"discriminator-actor-critic-addressing-sample","repo_url":"https://github.com/illidanlab/opolo-code","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"discriminator-actor-critic-addressing-sample","repo_url":"https://github.com/illidanlab/sail","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"imitation-learning","task_name":"Imitation Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.02925","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.02925"}},"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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