Quasi-Equivalence Discovery for Zero-Shot Emergent Communication

• URL: http://arxiv.org/abs/2103.08067v1
• Date: Sun, 14 Mar 2021 23:42:37 GMT
• Title: Quasi-Equivalence Discovery for Zero-Shot Emergent Communication
• Authors: Kalesha Bullard, Douwe Kiela, Joelle Pineau, Jakob Foerster
• Abstract summary: 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.
• Score: 63.175848843466845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective communication is an important skill for enabling information exchange in multi-agent settings and emergent communication is now a vibrant field of research, with common settings involving discrete cheap-talk channels. Since, by definition, these settings involve arbitrary encoding of information, typically they do not allow for the learned protocols to generalize beyond training partners. In contrast, in this work, we present a novel problem setting and the Quasi-Equivalence Discovery (QED) algorithm that allows for zero-shot coordination (ZSC), i.e., discovering protocols that can generalize to independently trained agents. Real world problem settings often contain costly communication channels, e.g., robots have to physically move their limbs, and a non-uniform distribution over intents. We show that these two factors lead to unique optimal ZSC policies in referential games, where agents use the energy cost of the messages to communicate intent. Other-Play was recently introduced for learning optimal ZSC policies, but requires prior access to the symmetries of the problem. Instead, QED can iteratively discovers the symmetries in this setting and converges to the optimal ZSC policy.

Related papers

• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users [95.77678166036561]
  We propose a UCB-type algorithm with delicate communication protocols. We give sub-linear regret bounds on par with those achieved in the synchronous framework. Empirical evaluation on synthetic and real-world datasets validates our algorithm's superior performance in terms of regrets and communication costs.
  arXiv  Detail & Related papers  (2024-02-26T05:31:14Z)
• Learning Translations: Emergent Communication Pretraining for Cooperative Language Acquisition [0.7832189413179361]
  In Emergent Communication (EC) agents learn to communicate with one another, but the protocols that they develop are specialised to their training community. This observation led to research into Zero-Shot Coordination (ZSC) for learning communication strategies that are robust to agents not encountered during training. We propose a novel AI challenge called a Cooperative Language Acquisition Problem (CLAP) in which the ZSC assumptions are relaxed by allowing a 'joiner' agent to learn from a dataset of interactions between agents in a target community.
  arXiv  Detail & Related papers  (2024-02-26T02:13:36Z)
• Learning to Cooperate and Communicate Over Imperfect Channels [27.241873614561538]
  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.
  arXiv  Detail & Related papers  (2023-11-24T12:15:48Z)
• Universally Expressive Communication in Multi-Agent Reinforcement Learning [6.086083595135936]
  We consider the question of whether a given communication protocol can express an arbitrary policy. With standard GNN approaches provably limited in their expressive capacity, we consider augmenting agent observations with: (1) unique agent IDs and (2) random noise. We provide a theoretical analysis as to how these approaches yield universally expressive communication, and also prove them capable of targeting arbitrary sets of actions for identical agents.
  arXiv  Detail & Related papers  (2022-06-14T11:16:33Z)
• 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)
• Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling [57.271494741212166]
  This paper formulates and solves the communication time minimization problem. It is found that the optimized policy gradually turns its priority from suppressing the remaining communication rounds to reducing per-round latency as the training process evolves. The effectiveness of the proposed scheme is demonstrated via a use case on collaborative 3D objective detection in autonomous driving.
  arXiv  Detail & Related papers  (2021-07-24T11:39:17Z)
• 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)

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.