Implicit Repair with Reinforcement Learning in Emergent Communication

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.

• The paper demonstrates that reinforcement learning drives the emergence of redundant protocols that implement implicit repair to mitigate communication errors.
• It introduces the Noisy Lewis Game where agents learn to overcome token masking and input noise in complex multi-distractor environments.
• The study shows that noise-resilient protocols generalize well, maintaining high task success in both noisy and ideal conditions while outperforming supervised baselines.

Implicit conversational repair, within the context of emergent communication, refers to the strategy employed by a sending agent (Speaker) to structure its message in a way that preemptively mitigates potential miscommunication or information loss, particularly in noisy environments. Unlike explicit repair, which involves back-and-forth clarification dialogues after a communication breakdown is detected, implicit repair aims to ensure the receiving agent (Listener) can successfully interpret the message despite potential corruption, often by encoding information redundantly. The work "Implicit Repair with Reinforcement Learning in Emergent Communication" (2502.12624) investigates how such implicit repair mechanisms, specifically through redundancy, can emerge naturally when agents learn to communicate using reinforcement learning in environments subject to noise.

Experimental Framework: The Noisy Lewis Game (NLG)

The research extends the standard Lewis signaling game (LG), a common paradigm for studying emergent communication. In the basic LG, a Speaker agent observes a target object (e.g., an image) and must generate a symbolic message to allow a Listener agent, observing the target and several distractor objects, to identify the target. The study introduces the Noisy Lewis Game (NLG) to model environmental pressures that necessitate robust communication.

• Game Setup: The experiments utilize complex visual inputs (ImageNet, CelebA datasets) and a substantial number of distractor items ($C$) to create a challenging communication task.
• Channel Noise Model: The primary source of noise is introduced in the communication channel. After the Speaker generates a message $m = (m_1, m_2, ..., m_L)$ of length $L$, each token $m_i$ has a probability $\lambda$ of being replaced by a special 'unk' token before reaching the Listener. The Speaker is unaware of which, if any, tokens are masked. This models scenarios where parts of a message might be lost or corrupted during transmission. The training noise level $\lambda$ is varied (e.g., 0.25, 0.5, 0.75) to study its impact.
• Input Noise Model (Evaluation): For evaluating out-of-distribution robustness, an additional noise modality is introduced during testing. Gaussian noise is added to the pre-computed image representations (e.g., ResNet features) provided to both the Speaker (target image) and the Listener (target and distractor images). This tests the protocol's resilience to perturbations in the agents' perceptual inputs.

Reinforcement Learning for Emergent Implicit Repair

The core mechanism driving the emergence of implicit repair is the application of reinforcement learning (RL) to train both the Speaker and Listener agents within the NLG framework.

• Agent Architectures: Both Speaker and Listener are implemented as neural networks, typically employing LSTMs or Transformers to process sequential message data and attention mechanisms to relate messages to visual inputs.
• Learning Algorithm: The agents are trained using the Reinforce policy gradient algorithm, operating under the Reinforcement Learning for Independent Agents (RIAL) paradigm. In RIAL, each agent treats the other agent(s) as part of its environment and learns its policy independently to maximize its own expected return, which in this cooperative game setting is the shared game reward. Actor-critic variants are used to stabilize learning.
• Reward Signal: The agents receive a sparse reward based solely on task success: +1 if the Listener correctly identifies the target image specified by the Speaker, and -1 otherwise. Crucially, there is no explicit signal indicating communication failure due to noise or providing information about how the message was corrupted.
• Incentivizing Robustness: The Speaker's objective is to maximize the probability of task success. In the NLG, success depends on the Listener receiving a sufficiently informative message despite potential token masking. Since the Speaker does not know which tokens will be masked, the Reinforce algorithm, driven by the sparse task reward, implicitly incentivizes the Speaker to develop a policy (message generation strategy) that is robust to this uncertainty. Through exploration and exploitation, the Speaker learns that encoding information redundantly across the message increases the likelihood that the Listener can reconstruct the intended meaning even if parts are lost, thereby maximizing the expected cumulative reward. Simultaneously, the Listener learns to interpret these potentially noisy and redundant messages to identify the target effectively. The mutual adaptation of Speaker and Listener policies under RL in the noisy channel leads to the emergence of a communication protocol embodying implicit repair.

Emergence of Redundancy as Implicit Repair

The primary finding of the research is that agents trained in the NLG using RL spontaneously develop communication protocols that utilize redundancy to combat channel noise, effectively implementing implicit repair.

• Mechanism: Redundancy ensures that the critical information needed to identify the target is distributed across multiple parts of the message. If some tokens are masked by channel noise, the remaining tokens still contain enough information for the Listener to succeed. This contrasts with protocols learned in noiseless environments (LG), which tend to be more brittle and informationally dense, where the loss of even a single token can lead to communication failure.
• Experimental Evidence: The paper demonstrates this by evaluating the robustness of learned communication protocols to systematic message masking at test time. For protocols developed in the NLG (specifically, NLG-RL where both agents are trained with RL), task success remains high even when a significant fraction (up to 50%) of the message tokens are artificially masked. In contrast, protocols learned in the standard LG (both LG-S, with a supervised Listener, and LG-RL, with RL agents but no noise during training) exhibit a sharp drop in performance even with minimal masking (e.g., masking just one token). This differential robustness provides strong evidence that NLG training induces redundancy.
• Quantification: The degree of robustness typically correlates with the level of noise ($\lambda$) experienced during training. Agents trained with higher $\lambda$ develop protocols that are resilient to higher levels of masking at test time.

Performance and Generalization

The study compares the NLG-RL approach against baselines, particularly LG-S (Supervised Listener in noiseless LG) and LG-RL (RL agents in noiseless LG).

• Robustness: NLG-RL significantly outperforms LG-S and LG-RL in noisy conditions (both channel noise and input noise at test time). Notably, NLG-RL protocols also perform comparably to the noiseless baselines when tested in a noiseless setting, indicating that the acquired robustness does not come at the cost of performance in ideal conditions. The paper claims its method is uniquely suited for producing protocols that handle both noisy and noiseless cases effectively.
• Comparison with Supervised Learning: The RL-based approach (LG-RL) is shown to outperform the LG-S baseline, especially as game complexity (e.g., number of distractors) increases, even in the absence of noise. This suggests RL is a more effective learning paradigm for complex emergent communication tasks.
• Generalization: The emergent communication protocols developed in the NLG demonstrate generalization capabilities comparable to those developed in simpler, deterministic LG settings. This implies that the introduction of noise and the resulting implicit repair mechanisms do not hinder the protocol's ability to generalize to unseen inputs (though the specific generalization metrics may need consideration). The robustness to input noise further supports the generalization capacity of the learned representations and communication strategy.

In conclusion, the research demonstrates that training multi-agent communication systems using reinforcement learning within a noisy environment (the NLG) effectively leads to the emergence of implicit repair strategies. This repair manifests as redundancy in the learned communication protocol, allowing agents to maintain high task success rates despite significant levels of channel noise or input perturbations, without compromising performance in noiseless conditions or hindering generalization. The use of the Reinforce algorithm with a sparse task reward is sufficient to drive this adaptation, highlighting RL's capability in fostering robust emergent communication.