Robust Event-Triggered Integrated Communication and Control with Graph Information Bottleneck Optimization (2502.09846v2)

Abstract: Integrated communication and control serves as a critical ingredient in Multi-Agent Reinforcement Learning. However, partial observability limitations will impair collaboration effectiveness, and a potential solution is to establish consensus through well-calibrated latent variables obtained from neighboring agents. Nevertheless, the rigid transmission of less informative content can still result in redundant information exchanges. Therefore, we propose a Consensus-Driven Event-Based Graph Information Bottleneck (CDE-GIB) method, which integrates the communication graph and information flow through a GIB regularizer to extract more concise message representations while avoiding the high computational complexity of inner-loop operations. To further minimize the communication volume required for establishing consensus during interactions, we also develop a variable-threshold event-triggering mechanism. By simultaneously considering historical data and current observations, this mechanism capably evaluates the importance of information to determine whether an event should be triggered. Experimental results demonstrate that our proposed method outperforms existing state-of-the-art methods in terms of both efficiency and adaptability.

• The paper introduces CDE-GIB, optimizing MARL communication via Graph Information Bottleneck message compression and dynamic event-trigger timing control.
• The method leverages a Graph Information Bottleneck (GIB) principle adapted for multi-agent consensus to learn concise messages that retain information critical for collaboration while minimizing redundancy.
• A dynamic, variable-threshold event-triggering mechanism determines when agents transmit messages based on the importance of new information relative to shared knowledge, further reducing communication load.

The paper "Robust Event-Triggered Integrated Communication and Control with Graph Information Bottleneck Optimization" (2502.09846) addresses challenges in Multi-Agent Reinforcement Learning (MARL), specifically concerning integrated communication and control under partial observability. The core issue is enabling effective collaboration among agents when each agent only possesses incomplete information about the environment state. A common approach involves agents exchanging information to establish a consensus or shared understanding, often through latent variable representations derived from neighbors' observations. However, naive communication strategies can lead to excessive information exchange, transmitting redundant or low-value data, thus hindering efficiency. The paper proposes the Consensus-Driven Event-Based Graph Information Bottleneck (CDE-GIB) method to mitigate these issues by optimizing both the content and the timing of inter-agent communication.

Consensus-Driven Event-Based Graph Information Bottleneck (CDE-GIB)

The CDE-GIB method introduces a novel approach to structure and regulate information flow in MARL systems. It leverages a Graph Information Bottleneck (GIB) principle, adapted for the multi-agent consensus setting, to learn concise yet informative message representations.

Graph Information Bottleneck Integration: The core idea is to apply an information bottleneck constraint during the message generation process. Traditional information bottleneck methods aim to find a compressed representation $Z$ of an input $X$ that retains maximal information about a target variable $Y$, formalized as maximizing the mutual information $I(Z; Y)$ while minimizing $I(Z; X)$. In the context of CDE-GIB, the input $X$ corresponds to an agent's local information (observations, internal state), the target $Y$ relates to the task objective or required consensus state, and $Z$ is the message to be transmitted.

The "Graph" aspect signifies that the communication structure (represented as a graph where nodes are agents and edges represent communication links) is explicitly incorporated into the bottleneck objective. This is likely achieved by using Graph Neural Networks (GNNs) to process local information and neighborhood messages, integrating the graph topology into the representation learning. The GIB regularizer encourages the learned message embeddings $Z$ to be minimal sufficient statistics for achieving consensus or coordinated action, conditioned on the communication graph structure. A key claim is that CDE-GIB avoids the computationally intensive inner-loop optimization often required in standard GIB formulations, potentially by employing approximations or specific network architectures. The objective function likely takes the form:

$$\max_{\theta} \mathbb{E}[R] - \beta I_{\theta}(Z; X | G)$$

where $\mathbb{E}[R]$ is the expected cumulative reward (the standard RL objective), $I_{\theta}(Z; X | G)$ is the mutual information between the generated message $Z$ and the agent's input $X$ given the communication graph $G$, and $\beta$ is a trade-off parameter controlling the compression level. The consensus aspect implies that the target variable $Y$ implicitly involves minimizing discrepancies between agents' latent representations or planned actions.

Consensus Mechanism: The method facilitates consensus by calibrating latent variables exchanged between neighboring agents. Agents learn to encode their relevant local information into these latent variables (messages), which are then processed, potentially via GNN aggregation, to form a shared understanding or coordinated policy input. The GIB framework ensures these latent variables are compressed representations focused on information critical for consensus and task execution.

Variable-Threshold Event-Triggering Mechanism

To further reduce communication overhead, CDE-GIB incorporates a dynamic event-triggering mechanism that determines when an agent should transmit its message. Unlike fixed-rate or simple threshold-based triggering, this mechanism adapts based on the evolving information context.

Information Importance Evaluation: The trigger condition is based on an evaluation of the "importance" of the information an agent currently possesses relative to the information previously shared or inferred by neighbors. This evaluation considers both the agent's current observation $o_t$ and historical data (e.g., previous messages $m_{t-k}$, past observations $o_{t-k}$, or an internal state $h_t$). The mechanism likely computes a metric representing the value or novelty of the potential message $m_t$ derived from $o_t$ and $h_t$. This could involve measuring the deviation from a predicted state, the potential impact on the consensus variable, or the estimated contribution to the global objective.

Variable Threshold: The threshold $\delta_t$ used to decide whether to trigger communication ($|| \text{ImportanceMetric}(m_t) || > \delta_t$) is not fixed. It dynamically adjusts based on factors such as the communication budget, the current system state volatility, or the convergence status of the consensus process. This allows the system to communicate more frequently during critical or uncertain phases and less frequently when the system is stable or communication yields diminishing returns. The precise update rule for $\delta_t$ is a key component of this mechanism, potentially learned or adapted heuristically.

Implementation and Optimization

Implementing CDE-GIB would typically involve a deep MARL framework where each agent's policy network is augmented with communication modules.

• Network Architecture: Agents would likely employ recurrent neural networks (RNNs, e.g., LSTMs or GRUs) to maintain internal states $h_t$ capturing historical information. A GNN layer would process incoming messages from neighbors and integrate them with the local observation $o_t$ and internal state $h_t$. An encoder network would generate the latent message $m_t$ based on $o_t$ and $h_t$, subject to the GIB regularization. The event-triggering logic would reside within each agent, potentially implemented as a small neural network or a rule-based system evaluating the importance metric against the dynamic threshold $\delta_t$.
• Optimization: Training involves optimizing the agent policies (actors) and potentially value functions (critics) alongside the communication components (encoder, GNN, trigger). The loss function would combine the RL objective (e.g., policy gradient loss, Q-learning loss) with the GIB regularization term. Estimating the mutual information term $I(Z; X | G)$ typically requires variational approximations or other estimation techniques. The parameters of the encoder, GNN, trigger mechanism, and policy/value networks are jointly optimized using gradient-based methods.
• Computational Complexity: While claiming to avoid GIB inner-loop complexities, the use of GNNs and potentially complex trigger mechanisms still implies significant computational requirements, especially for large numbers of agents. The communication cost reduction aims to offset this during deployment.

Experimental Validation

The abstract states that experimental results demonstrate CDE-GIB outperforms existing state-of-the-art (SOTA) methods in both efficiency (likely measured by task performance vs. communication volume/frequency) and adaptability (robustness to varying conditions or tasks). Without access to the full paper, specific benchmarks (e.g., StarCraft Multi-Agent Challenge, cooperative navigation, traffic control) and quantitative results (e.g., percentage reduction in communication bits, improvement in task success rate or cumulative reward) are not detailed here. The comparison against SOTA methods implies evaluation against other MARL communication strategies, such as CommNet, TarMAC, IC3Net, or potentially other GIB-based approaches if they exist in this context.

Conclusion

The CDE-GIB method presents a framework for optimizing inter-agent communication in MARL by integrating a Graph Information Bottleneck regularizer for message compression and a dynamic, variable-threshold event-triggering mechanism to minimize unnecessary transmissions. By jointly considering message content optimization via GIB and transmission timing via adaptive triggering, it aims to enhance both the efficiency and effectiveness of collaboration among agents operating under partial observability. The practical significance hinges on the claimed computational benefits over standard GIB and demonstrated performance gains in relevant MARL benchmarks.