{"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/learning-to-communicate-with-deep-multi-agent","title":"Learning to Communicate with Deep Multi-Agent Reinforcement Learning","arxiv_id":"1605.06676","date":"2016-05-21","proceeding":"NeurIPS 2016 12","authors":["Jakob N. Foerster","Yannis M. Assael","Nando de Freitas","Shimon Whiteson"],"abstract":"We consider the problem of multiple agents sensing and acting in environments\nwith the goal of maximising their shared utility. In these environments, agents\nmust learn communication protocols in order to share information that is needed\nto solve the tasks. By embracing deep neural networks, we are able to\ndemonstrate end-to-end learning of protocols in complex environments inspired\nby communication riddles and multi-agent computer vision problems with partial\nobservability. We propose two approaches for learning in these domains:\nReinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning\n(DIAL). The former uses deep Q-learning, while the latter exploits the fact\nthat, during learning, agents can backpropagate error derivatives through\n(noisy) communication channels. Hence, this approach uses centralised learning\nbut decentralised execution. Our experiments introduce new environments for\nstudying the learning of communication protocols and present a set of\nengineering innovations that are essential for success in these domains.","url_abs":"http://arxiv.org/abs/1605.06676v2","url_pdf":"http://arxiv.org/pdf/1605.06676v2.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-to-communicate-with-deep-multi-agent","repo_url":"https://github.com/emiliojorge/Inventing-a-Grounded-Language","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"learning-to-communicate-with-deep-multi-agent","repo_url":"https://github.com/iassael/learning-to-communicate","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"learning-to-communicate-with-deep-multi-agent","repo_url":"https://github.com/sharan-dce/DIAL-switch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"multi-agent-reinforcement-learning","task_name":"Multi-agent Reinforcement Learning"},{"task_slug":"q-learning","task_name":"Q-Learning"},{"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=1605.06676","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}