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In this paper we explore a simple neural model, called\nCommNet, that uses continuous communication for fully cooperative tasks. The\nmodel consists of multiple agents and the communication between them is learned\nalongside their policy. We apply this model to a diverse set of tasks,\ndemonstrating the ability of the agents to learn to communicate amongst\nthemselves, yielding improved performance over non-communicative agents and\nbaselines. In some cases, it is possible to interpret the language devised by\nthe agents, revealing simple but effective strategies for solving the task at\nhand.","url_abs":"http://arxiv.org/abs/1605.07736v2","url_pdf":"http://arxiv.org/pdf/1605.07736v2.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":"learning-multiagent-communication-with","repo_url":"https://github.com/Coac/CommNet-BiCnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"learning-multiagent-communication-with","repo_url":"https://github.com/MUmarJaved/MultiAgent-Distributed-Reinforcement-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"learning-multiagent-communication-with","repo_url":"https://github.com/anonymous1234517/code","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"learning-multiagent-communication-with","repo_url":"https://github.com/cts198859/deeprl_dist","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"learning-multiagent-communication-with","repo_url":"https://github.com/cts198859/deeprl_network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"learning-multiagent-communication-with","repo_url":"https://github.com/dongchen06/macacc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"learning-multiagent-communication-with","repo_url":"https://github.com/facebookarchive/commnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"learning-multiagent-communication-with","repo_url":"https://github.com/facebookresearch/CommNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"learning-multiagent-communication-with","repo_url":"https://github.com/isp1tze/MAProj","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1605.07736","atlas_url":"https://app.syntology.ai/?focus=1605.07736","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.07736"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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