{"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/vime-variational-information-maximizing","title":"VIME: Variational Information Maximizing Exploration","arxiv_id":"1605.09674","date":"2016-05-31","proceeding":"NeurIPS 2016 12","authors":["Rein Houthooft","Xi Chen","Yan Duan","John Schulman","Filip De Turck","Pieter Abbeel"],"abstract":"Scalable and effective exploration remains a key challenge in reinforcement\nlearning (RL). While there are methods with optimality guarantees in the\nsetting of discrete state and action spaces, these methods cannot be applied in\nhigh-dimensional deep RL scenarios. As such, most contemporary RL relies on\nsimple heuristics such as epsilon-greedy exploration or adding Gaussian noise\nto the controls. This paper introduces Variational Information Maximizing\nExploration (VIME), an exploration strategy based on maximization of\ninformation gain about the agent's belief of environment dynamics. We propose a\npractical implementation, using variational inference in Bayesian neural\nnetworks which efficiently handles continuous state and action spaces. VIME\nmodifies the MDP reward function, and can be applied with several different\nunderlying RL algorithms. We demonstrate that VIME achieves significantly\nbetter performance compared to heuristic exploration methods across a variety\nof continuous control tasks and algorithms, including tasks with very sparse\nrewards.","url_abs":"http://arxiv.org/abs/1605.09674v4","url_pdf":"http://arxiv.org/pdf/1605.09674v4.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":"vime-variational-information-maximizing","repo_url":"https://github.com/alec-tschantz/vime","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"vime-variational-information-maximizing","repo_url":"https://github.com/openai/vime","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"continuous-control","task_name":"Continuous Control"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"variational-inference","task_name":"Variational Inference"},{"task_slug":"continuous-control","task_name":"continuous-control"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1605.09674","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.09674"}},"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/openai/vime","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/alec-tschantz/vime","reach":null}],"summary":{"ran_honours":1},"by_repo_kind":{"listed":{"samples":1,"ran":1,"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":1,"samples":[{"code_sha256_prefix":"bbb651776e5c3c7c","entry":"eval_inf_gain","repo":"alec-tschantz/vime","repo_kind":"listed","path":"train_dynamics.py","file_url":"https://github.com/alec-tschantz/vime/blob/HEAD/train_dynamics.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"bbb651776e5c3c7c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}