{"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/the-differentiable-cross-entropy-method-1","title":"The Differentiable Cross-Entropy Method","arxiv_id":"1909.12830","date":"2019-09-27","proceeding":"ICML 2020 1","authors":["Brandon Amos","Denis Yarats"],"abstract":"We study the cross-entropy method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant that enables us to differentiate the output of CEM with respect to the objective function's parameters. In the machine learning setting this brings CEM inside of the end-to-end learning pipeline where this has otherwise been impossible. We show applications in a synthetic energy-based structured prediction task and in non-convex continuous control. In the control setting we show how to embed optimal action sequences into a lower-dimensional space. DCEM enables us to fine-tune CEM-based controllers with policy optimization.","url_abs":"https://arxiv.org/abs/1909.12830v4","url_pdf":"https://arxiv.org/pdf/1909.12830v4.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":"the-differentiable-cross-entropy-method-1","repo_url":"https://github.com/facebookresearch/dcem","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"continuous-control","task_name":"Continuous Control"},{"task_slug":"structured-prediction","task_name":"Structured Prediction"},{"task_slug":"continuous-control","task_name":"continuous-control"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1909.12830","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.12830"}},"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/facebookresearch/dcem","reach":null}],"summary":{"ran_fixture":1,"ran_honours":1,"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":3,"ran":3,"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":3,"samples":[{"code_sha256_prefix":"9167ec11eb408437","entry":"rew_nominal","repo":"facebookresearch/dcem","repo_kind":"official","path":"exps/cartpole_emb.py","file_url":"https://github.com/facebookresearch/dcem/blob/HEAD/exps/cartpole_emb.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"9167ec11eb408437"}},{"code_sha256_prefix":"95fd76d41354f1aa","entry":"rew_step","repo":"facebookresearch/dcem","repo_kind":"official","path":"exps/cartpole_emb.py","file_url":"https://github.com/facebookresearch/dcem/blob/HEAD/exps/cartpole_emb.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"95fd76d41354f1aa"}},{"code_sha256_prefix":"4d87e0b22738782f","entry":"uniform","repo":"facebookresearch/dcem","repo_kind":"official","path":"exps/cartpole_emb.py","file_url":"https://github.com/facebookresearch/dcem/blob/HEAD/exps/cartpole_emb.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"4d87e0b22738782f"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}