Implicit Repair with Reinforcement Learning in Emergent Communication

• URL: http://arxiv.org/abs/2502.12624v1
• Date: Tue, 18 Feb 2025 08:09:53 GMT
• Title: Implicit Repair with Reinforcement Learning in Emergent Communication
• Authors: Fábio Vital, Alberto Sardinha, Francisco S. Melo,
• Abstract summary: We focus on extending the signaling game, called the Lewis Game, by adding noise in the communication channel and inputs received by the agents. Our analysis shows that agents add redundancy to the transmitted messages as an outcome to prevent the negative impact of noise on the task success.
• Score: 3.8779763612314633
• License:
• Abstract: Conversational repair is a mechanism used to detect and resolve miscommunication and misinformation problems when two or more agents interact. One particular and underexplored form of repair in emergent communication is the implicit repair mechanism, where the interlocutor purposely conveys the desired information in such a way as to prevent misinformation from any other interlocutor. This work explores how redundancy can modify the emergent communication protocol to continue conveying the necessary information to complete the underlying task, even with additional external environmental pressures such as noise. We focus on extending the signaling game, called the Lewis Game, by adding noise in the communication channel and inputs received by the agents. Our analysis shows that agents add redundancy to the transmitted messages as an outcome to prevent the negative impact of noise on the task success. Additionally, we observe that the emerging communication protocol's generalization capabilities remain equivalent to architectures employed in simpler games that are entirely deterministic. Additionally, our method is the only one suitable for producing robust communication protocols that can handle cases with and without noise while maintaining increased generalization performance levels.

Related papers

• Secure Semantic Communication via Paired Adversarial Residual Networks [59.468221305630784]
  This letter explores the positive side of the adversarial attack for the security-aware semantic communication system. A pair of matching pluggable modules is installed: one after the semantic transmitter and the other before the semantic receiver. The proposed scheme is capable of fooling the eavesdropper while maintaining the high-quality semantic communication.
  arXiv  Detail & Related papers  (2024-07-02T08:32:20Z)
• Emergent Quantized Communication [34.31732248872158]
  We propose an alternative approach to achieve discrete communication -- quantization of communicated messages. Message quantization allows us to train the model end-to-end, achieving superior performance in multiple setups.
  arXiv  Detail & Related papers  (2022-11-04T12:39:45Z)
• Over-communicate no more: Situated RL agents learn concise communication protocols [78.28898217947467]
  It is unclear how to design artificial agents that can learn to effectively and efficiently communicate with each other. Much research on communication emergence uses reinforcement learning (RL) We explore situated communication in a multi-step task, where the acting agent has to forgo an environmental action to communicate. We find that while all tested pressures can disincentivise over-communication, situated communication does it most effectively and, unlike the cost on effort, does not negatively impact emergence.
  arXiv  Detail & Related papers  (2022-11-02T21:08:14Z)
• Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems [51.6210785955659]
  Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions. However, when deploying trained communicative agents in a real-world application where noise and potential attackers exist, the safety of communication-based policies becomes a severe issue that is underexplored. In this work, we consider an environment with $N$ agents, where the attacker may arbitrarily change the communication from any $CfracN-12$ agents to a victim agent.
  arXiv  Detail & Related papers  (2022-06-21T07:32:18Z)
• The Enforcers: Consistent Sparse-Discrete Methods for Constraining Informative Emergent Communication [5.432350993419402]
  Communication enables agents to cooperate to achieve their goals. Recent work in learning sparse communication suffers from high variance training where, the price of decreasing communication is a decrease in reward, particularly in cooperative tasks. This research addresses the above issues by limiting the loss in reward of decreasing communication and eliminating the penalty for discretization.
  arXiv  Detail & Related papers  (2022-01-19T07:31:06Z)
• Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents [83.52684405389445]
  We introduce the collaborative multi-object navigation task CoMON. In this task, an oracle agent has detailed environment information in the form of a map. It communicates with a navigator agent that perceives the environment visually and is tasked to find a sequence of goals. We show that the emergent communication can be grounded to the agent observations and the spatial structure of the 3D environment.
  arXiv  Detail & Related papers  (2021-10-12T06:56:11Z)
• Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels [0.0]
  We propose a novel formulation of the "effectiveness problem" in communications. We consider multiple agents communicating over a noisy channel in order to achieve better coordination and cooperation. We show via examples that the joint policy learned using the proposed framework is superior to that where the communication is considered separately.
  arXiv  Detail & Related papers  (2021-01-02T10:43:41Z)
• Learning to Communicate and Correct Pose Errors [75.03747122616605]
  We study the setting proposed in V2VNet, where nearby self-driving vehicles jointly perform object detection and motion forecasting in a cooperative manner. We propose a novel neural reasoning framework that learns to communicate, to estimate potential errors, and to reach a consensus about those errors.
  arXiv  Detail & Related papers  (2020-11-10T18:19:40Z)
• 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)
• Learning to Communicate Using Counterfactual Reasoning [2.8110705488739676]
  This paper introduces the novel multi-agent counterfactual communication learning (MACC) method. MACC adapts counterfactual reasoning in order to overcome the credit assignment problem for communicating agents. Our experiments show that MACC is able to outperform the state-of-the-art baselines in four different scenarios in the Particle environment.
  arXiv  Detail & Related papers  (2020-06-12T14:02:04Z)
• Learning Individually Inferred Communication for Multi-Agent Cooperation [37.56115000150748]
  We propose Individually Inferred Communication (I2C) to enable agents to learn a prior for agent-agent communication. The prior knowledge is learned via causal inference and realized by a feed-forward neural network. I2C can not only reduce communication overhead but also improve the performance in a variety of multi-agent cooperative scenarios.
  arXiv  Detail & Related papers  (2020-06-11T14:07:57Z)

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.