{"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/symbolic-distillation-for-learned-tcp","title":"Symbolic Distillation for Learned TCP Congestion Control","arxiv_id":"2210.16987","date":"2022-10-24","proceeding":null,"authors":["S P Sharan","Wenqing Zheng","Kuo-Feng Hsu","Jiarong Xing","Ang Chen","Zhangyang Wang"],"abstract":"Recent advances in TCP congestion control (CC) have achieved tremendous success with deep reinforcement learning (RL) approaches, which use feedforward neural networks (NN) to learn complex environment conditions and make better decisions. However, such \"black-box\" policies lack interpretability and reliability, and often, they need to operate outside the traditional TCP datapath due to the use of complex NNs. This paper proposes a novel two-stage solution to achieve the best of both worlds: first to train a deep RL agent, then distill its (over-)parameterized NN policy into white-box, light-weight rules in the form of symbolic expressions that are much easier to understand and to implement in constrained environments. At the core of our proposal is a novel symbolic branching algorithm that enables the rule to be aware of the context in terms of various network conditions, eventually converting the NN policy into a symbolic tree. The distilled symbolic rules preserve and often improve performance over state-of-the-art NN policies while being faster and simpler than a standard neural network. We validate the performance of our distilled symbolic rules on both simulation and emulation environments. Our code is available at https://github.com/VITA-Group/SymbolicPCC.","url_abs":"https://arxiv.org/abs/2210.16987v1","url_pdf":"https://arxiv.org/pdf/2210.16987v1.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":"symbolic-distillation-for-learned-tcp","repo_url":"https://github.com/vita-group/symbolicpcc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"}],"methods":[{"method_slug":"aware","method_name":"AWARE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2210.16987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.16987"}},"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":"deterministic:regex_extraction","url":"https://github.com/VITA-Group/SymbolicPCC","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/vita-group/symbolicpcc","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":6},"by_repo_kind":{"official":{"samples":6,"ran":0,"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":"5caae64c0705fd7a","entry":"apply_rate_delta","repo":"VITA-Group/SymbolicPCC","repo_kind":"official","path":"udt-plugins/testing/loaded_client.py","file_url":"https://github.com/VITA-Group/SymbolicPCC/blob/HEAD/udt-plugins/testing/loaded_client.py","link_basis":"harvester_set","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":"5caae64c0705fd7a"}},{"code_sha256_prefix":"a66debf6e90a77f1","entry":"arg_or_default","repo":"VITA-Group/SymbolicPCC","repo_kind":"official","path":"common/simple_arg_parse.py","file_url":"https://github.com/VITA-Group/SymbolicPCC/blob/HEAD/common/simple_arg_parse.py","link_basis":"harvester_set","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":"a66debf6e90a77f1"}},{"code_sha256_prefix":"8ad93e0d9425b1cb","entry":"get_max_obs_vector","repo":"VITA-Group/SymbolicPCC","repo_kind":"official","path":"common/sender_obs.py","file_url":"https://github.com/VITA-Group/SymbolicPCC/blob/HEAD/common/sender_obs.py","link_basis":"harvester_set","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":"8ad93e0d9425b1cb"}},{"code_sha256_prefix":"1ba498095f1256f2","entry":"get_min_obs_vector","repo":"VITA-Group/SymbolicPCC","repo_kind":"official","path":"common/sender_obs.py","file_url":"https://github.com/VITA-Group/SymbolicPCC/blob/HEAD/common/sender_obs.py","link_basis":"harvester_set","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":"1ba498095f1256f2"}},{"code_sha256_prefix":"4b1c41a5cc6b3cbc","entry":"get_rate","repo":"VITA-Group/SymbolicPCC","repo_kind":"official","path":"udt-plugins/testing/skeleton_client.py","file_url":"https://github.com/VITA-Group/SymbolicPCC/blob/HEAD/udt-plugins/testing/skeleton_client.py","link_basis":"harvester_set","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":"4b1c41a5cc6b3cbc"}},{"code_sha256_prefix":"a1c99f0f6f04af5f","entry":"get_rate","repo":"VITA-Group/SymbolicPCC","repo_kind":"official","path":"udt-plugins/training/shim.py","file_url":"https://github.com/VITA-Group/SymbolicPCC/blob/HEAD/udt-plugins/training/shim.py","link_basis":"harvester_set","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":"a1c99f0f6f04af5f"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}