{"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-task-maximum-entropy-inverse","title":"Multi-task Maximum Entropy Inverse Reinforcement Learning","arxiv_id":"1805.08882","date":"2018-05-22","proceeding":null,"authors":["Adam Gleave","Oliver Habryka"],"abstract":"Multi-task Inverse Reinforcement Learning (IRL) is the problem of inferring\nmultiple reward functions from expert demonstrations. Prior work, built on\nBayesian IRL, is unable to scale to complex environments due to computational\nconstraints. This paper contributes a formulation of multi-task IRL in the more\ncomputationally efficient Maximum Causal Entropy (MCE) IRL framework.\nExperiments show our approach can perform one-shot imitation learning in a\ngridworld environment that single-task IRL algorithms need hundreds of\ndemonstrations to solve. We outline preliminary work using meta-learning to\nextend our method to the function approximator setting of modern MCE IRL\nalgorithms. Evaluating on multi-task variants of common simulated robotics\nbenchmarks, we discover serious limitations of these IRL algorithms, and\nconclude with suggestions for further work.","url_abs":"http://arxiv.org/abs/1805.08882v2","url_pdf":"http://arxiv.org/pdf/1805.08882v2.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-task-maximum-entropy-inverse","repo_url":"https://github.com/HumanCompatibleAI/population-irl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"imitation-learning","task_name":"Imitation Learning"},{"task_slug":"meta-learning","task_name":"Meta-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":{"atlas_url":"https://app.syntology.ai/?focus=1805.08882","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.08882"}},"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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