{"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/randomized-exploration-for-reinforcement","title":"Randomized Exploration for Reinforcement Learning with General Value Function Approximation","arxiv_id":"2106.07841","date":"2021-06-15","proceeding":null,"authors":["Haque Ishfaq","Qiwen Cui","Viet Nguyen","Alex Ayoub","Zhuoran Yang","Zhaoran Wang","Doina Precup","Lin F. Yang"],"abstract":"We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm as well as the optimism principle. Unlike existing upper-confidence-bound (UCB) based approaches, which are often computationally intractable, our algorithm drives exploration by simply perturbing the training data with judiciously chosen i.i.d. scalar noises. To attain optimistic value function estimation without resorting to a UCB-style bonus, we introduce an optimistic reward sampling procedure. When the value functions can be represented by a function class $\\mathcal{F}$, our algorithm achieves a worst-case regret bound of $\\widetilde{O}(\\mathrm{poly}(d_EH)\\sqrt{T})$ where $T$ is the time elapsed, $H$ is the planning horizon and $d_E$ is the $\\textit{eluder dimension}$ of $\\mathcal{F}$. In the linear setting, our algorithm reduces to LSVI-PHE, a variant of RLSVI, that enjoys an $\\widetilde{\\mathcal{O}}(\\sqrt{d^3H^3T})$ regret. We complement the theory with an empirical evaluation across known difficult exploration tasks.","url_abs":"https://arxiv.org/abs/2106.07841v2","url_pdf":"https://arxiv.org/pdf/2106.07841v2.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":"randomized-exploration-for-reinforcement","repo_url":"https://github.com/qlan3/Explorer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"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=2106.07841","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.07841"}},"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/qlan3/Explorer","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":1,"unverified":3},"by_repo_kind":{"official":{"samples":4,"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":0,"samples":[{"code_sha256_prefix":"3a9ab37bf0498c42","entry":"layer_init","repo":"qlan3/Explorer","repo_kind":"official","path":"components/network.py","file_url":"https://github.com/qlan3/Explorer/blob/HEAD/components/network.py","link_basis":"plan_row","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3a9ab37bf0498c42"}},{"code_sha256_prefix":"11ed4906b14eda12","entry":"get_csv_result_dict","repo":"qlan3/Explorer","repo_kind":"official","path":"analysis.py","file_url":"https://github.com/qlan3/Explorer/blob/HEAD/analysis.py","link_basis":"first_harvest_node","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":"11ed4906b14eda12"}},{"code_sha256_prefix":"d546fcbe91812f14","entry":"get_process_result_dict","repo":"qlan3/Explorer","repo_kind":"official","path":"analysis.py","file_url":"https://github.com/qlan3/Explorer/blob/HEAD/analysis.py","link_basis":"first_harvest_node","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":"d546fcbe91812f14"}},{"code_sha256_prefix":"9268a3c705e02eda","entry":"x_format","repo":"qlan3/Explorer","repo_kind":"official","path":"plot.py","file_url":"https://github.com/qlan3/Explorer/blob/HEAD/plot.py","link_basis":"first_harvest_node","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":"9268a3c705e02eda"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}