Learning to Cooperate and Communicate Over Imperfect Channels

• URL: http://arxiv.org/abs/2311.14770v1
• Date: Fri, 24 Nov 2023 12:15:48 GMT
• Title: Learning to Cooperate and Communicate Over Imperfect Channels
• Authors: Jannis Weil, Gizem Ekinci, Heinz Koeppl, Tobias Meuser
• Abstract summary: We consider a cooperative multi-agent system where the agents act and exchange information in a decentralized manner using a limited and unreliable channel. Our method allows agents to dynamically adapt how much information to share by sending messages of different sizes. We show that our approach outperforms approaches without adaptive capabilities in a novel cooperative digit-prediction environment.
• Score: 27.241873614561538
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Information exchange in multi-agent systems improves the cooperation among agents, especially in partially observable settings. In the real world, communication is often carried out over imperfect channels. This requires agents to handle uncertainty due to potential information loss. In this paper, we consider a cooperative multi-agent system where the agents act and exchange information in a decentralized manner using a limited and unreliable channel. To cope with such channel constraints, we propose a novel communication approach based on independent Q-learning. Our method allows agents to dynamically adapt how much information to share by sending messages of different sizes, depending on their local observations and the channel's properties. In addition to this message size selection, agents learn to encode and decode messages to improve their jointly trained policies. We show that our approach outperforms approaches without adaptive capabilities in a novel cooperative digit-prediction environment and discuss its limitations in the traffic junction environment.

Related papers

• Communication Learning in Multi-Agent Systems from Graph Modeling Perspective [62.13508281188895]
  We introduce a novel approach wherein we conceptualize the communication architecture among agents as a learnable graph. We introduce a temporal gating mechanism for each agent, enabling dynamic decisions on whether to receive shared information at a given time.
  arXiv  Detail & Related papers  (2024-11-01T05:56:51Z)
• Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control [13.844458247041711]
  Traffic signal control (TSC) is a challenging problem within intelligent transportation systems. We propose a communication-based MARL framework for large-scale TSC. Our framework allows each agent to learn a communication policy that dictates "which" part of the message is sent "to whom"
  arXiv  Detail & Related papers  (2023-10-03T21:06:51Z)
• Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel [81.39444892747512]
  In Multi-Agent Reinforcement Learning (MARL), specialized channels are often introduced that allow agents to communicate directly with one another. We propose an alternative approach whereby agents communicate through an intelligent facilitator that learns to sift through and interpret signals provided by all agents to improve the agents' collective performance.
  arXiv  Detail & Related papers  (2022-05-21T14:11:33Z)
• Distributed Adaptive Learning Under Communication Constraints [54.22472738551687]
  This work examines adaptive distributed learning strategies designed to operate under communication constraints. We consider a network of agents that must solve an online optimization problem from continual observation of streaming data.
  arXiv  Detail & Related papers  (2021-12-03T19:23:48Z)
• Learning Selective Communication for Multi-Agent Path Finding [18.703918339797283]
  Decision Causal Communication (DCC) is a simple yet efficient model to enable agents to select neighbors to conduct communication. DCC is suitable for decentralized execution to handle large scale problems.
  arXiv  Detail & Related papers  (2021-09-12T03:07:20Z)
• Quasi-Equivalence Discovery for Zero-Shot Emergent Communication [63.175848843466845]
  We present a novel problem setting and the Quasi-Equivalence Discovery algorithm that allows for zero-shot coordination (ZSC) We show that these two factors lead to unique optimal ZSC policies in referential games. QED can iteratively discover the symmetries in this setting and converges to the optimal ZSC policy.
  arXiv  Detail & Related papers  (2021-03-14T23:42:37Z)
• Adversarial Attacks On Multi-Agent Communication [80.4392160849506]
  Modern autonomous systems will soon be deployed at scale, opening up the possibility for cooperative multi-agent systems. Such advantages rely heavily on communication channels which have been shown to be vulnerable to security breaches. In this paper, we explore such adversarial attacks in a novel multi-agent setting where agents communicate by sharing learned intermediate representations.
  arXiv  Detail & Related papers  (2021-01-17T00:35:26Z)
• Gaussian Process Based Message Filtering for Robust Multi-Agent Cooperation in the Presence of Adversarial Communication [5.161531917413708]
  We consider the problem of providing robustness to adversarial communication in multi-agent systems. We propose a communication architecture based on Graph Neural Networks (GNNs) We show that our filtering method is able to reduce the impact that non-cooperative agents cause.
  arXiv  Detail & Related papers  (2020-12-01T14:21:58Z)
• Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations [59.608216900601384]
  We study agents that learn to communicate via actuating their joints in a 3D environment. We show that under realistic assumptions, a non-uniform distribution of intents and a common-knowledge energy cost, these agents can find protocols that generalize to novel partners.
  arXiv  Detail & Related papers  (2020-10-29T19:23:10Z)
• The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning [6.18778092044887]
  Many real-world problems require the coordination of multiple autonomous agents. Recent work has shown the promise of Graph Neural Networks (GNNs) to learn explicit communication strategies that enable complex multi-agent coordination. We show how a single self-interested agent is capable of learning highly manipulative communication strategies that allows it to significantly outperform a cooperative team of agents.
  arXiv  Detail & Related papers  (2020-08-06T12:48:08Z)
• Learning Multi-Agent Coordination through Connectivity-driven Communication [7.462336024223669]
  In artificial multi-agent systems, the ability to learn collaborative policies is predicated upon the agents' communication skills. We present a deep reinforcement learning approach, Connectivity Driven Communication (CDC) CDC is able to learn effective collaborative policies and can over-perform competing learning algorithms on cooperative navigation tasks.
  arXiv  Detail & Related papers  (2020-02-12T20:58:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.