{"ID":2845745,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.03348","arxiv_id":"2511.03348","title":"Learning Communication Skills in Multi-task Multi-agent Deep Reinforcement Learning","abstract":"In multi-agent deep reinforcement learning (MADRL), agents can communicate with one another to perform a task in a coordinated manner. When multiple tasks are involved, agents can also leverage knowledge from one task to improve learning in other tasks. In this paper, we propose Multi-task Communication Skills (MCS), a MADRL with communication method that learns and performs multiple tasks simultaneously, with agents interacting through learnable communication protocols. MCS employs a Transformer encoder to encode task-specific observations into a shared message space, capturing shared communication skills among agents. To enhance coordination among agents, we introduce a prediction network that correlates messages with the actions of sender agents in each task. We adapt three multi-agent benchmark environments to multi-task settings, where the number of agents as well as the observation and action spaces vary across tasks. Experimental results demonstrate that MCS achieves better performance than multi-task MADRL baselines without communication, as well as single-task MADRL baselines with and without communication.","short_abstract":"In multi-agent deep reinforcement learning (MADRL), agents can communicate with one another to perform a task in a coordinated manner. When multiple tasks are involved, agents can also leverage knowledge from one task to improve learning in other tasks. In this paper, we propose Multi-task Communication Skills (MCS), a...","url_abs":"https://arxiv.org/abs/2511.03348","url_pdf":"https://arxiv.org/pdf/2511.03348v2","authors":"[\"Changxi Zhu\",\"Mehdi Dastani\",\"Shihan Wang\"]","published":"2025-11-05T10:34:44Z","proceeding":"cs.MA","tasks":"[\"cs.MA\"]","methods":"[\"Reinforcement Learning\",\"Transformer\"]","has_code":false}