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Towards True Lossless Sparse Communication in Multi-Agent Systems
Seth Karten, Mycal Tucker, Siva Kailas, Katia Sycara
TL;DR
Sparse individualized communication can be unstable and costly in reward, despite its importance when bandwidth is limited. IMGS-MAC uses representation learning and message analysis to identify lossless budgets, with learned gating for tighter constraints.
Problem
Sparse individualized communication can be high variance, and reducing messages can decrease reward in cooperative settings with limited bandwidth.
Source: §1 · p. 2Method
Results
The authors report zero-shot sparsity without performance cost within the lossless range, while few-shot gating limits performance loss for over-constrained budgets.
Source: §6 · p. 9, §5.2.2 · p. 9Takeaways & Limitations
Abstract
from arXiv · showhide
Communication enables agents to cooperate to achieve their goals. Learning when to communicate, i.e., sparse (in time) communication, and whom to message is particularly important when bandwidth is limited. Recent work in learning sparse individualized communication, however, suffers from high variance during training, where decreasing communication comes at the cost of decreased reward, particularly in cooperative tasks. We use the information bottleneck to reframe sparsity as a representation learning problem, which we show naturally enables lossless sparse communication at lower budgets than prior art. In this paper, we propose a method for true lossless sparsity in communication via Information Maximizing Gated Sparse Multi-Agent Communication (IMGS-MAC). Our model uses two individualized regularization objectives, an information maximization autoencoder and sparse communication loss, to create informative and sparse communication. We evaluate the learned communication `language' through direct causal analysis of messages in non-sparse runs to determine the range of lossless sparse budgets, which allow zero-shot sparsity, and the range of sparse budgets that will inquire a reward loss, which is minimized by our learned gating function with few-shot sparsity. To demonstrate the efficacy of our results, we experiment in cooperative multi-agent tasks where communication is essential for success. We evaluate our model with both continuous and discrete messages. We focus our analysis on a variety of ablations to show the effect of message representations, including their properties, and lossless performance of our model.
1 Introduction
Communication supports cooperation under partial observability, but bandwidth limits make sparse messaging important. Prior sparse individualized methods can be unstable and trade lower communication for reward, motivating IMGS-MAC’s representation-learning approach to sparse communication.
1 Introduction
- Sparse communication is needed for bandwidth-limited cooperative settings, where agents may lack full environmental observations.A budget sets the maximum percentage of time an agent may communicate.Source: §1 · pp. 1–2
- Prior sparse communication methods can be unstable, sending unnecessary or missing necessary messages and reducing reward as communication decreases.The paper calls unnecessary messages “null communications.”Source: §1 · p. 2
- IMGS-MAC reframes sparsity as representation learning, using an information-maximizing autoencoder and message-effect analysis to remove null messages before enforcing budgets.A learned gate is finetuned for over-constrained budgets.Source: §1 · p. 2
2 Related Work
Prior work studies emergent continuous and discrete communication and sparse gating, while this paper emphasizes state-reconstruction-grounded representations and token-level message analysis.
2.1 Emergent Communication Vectors
- Grounding messages in reconstructing the concatenated state of all agents is presented as a way to create desirable representations in continuous and discrete settings.Prior discrete-communication methods often learn token meanings through rewards alone [12, 15, 6, 2, 7].Source: §2.1 · p. 3
2.2 Sparsity: Gating Total Messages
- Existing gating approaches have difficulty setting budgets or can be high variance; this work measures each token’s effect and masks the emergent vocabulary accordingly.I2C [4] addresses lossless sparse targeting but does not account for message representation.Source: §2.2 · p. 3
3 Preliminaries
The setup formalizes decentralized agents communicating under partial observability and frames sparsity as reducing messages while preserving task performance. The paper motivates learning message representations that support this lossless objective.
3 Preliminaries
- The problem is framed as centralized training with decentralized execution in a partially observable Markov decision process with individualized communication.The formal setup includes states, actions, messages, transitions, observations, rewards, and a discount factor.Source: §3 · p. 3
3.1 The Sparsity Objective
- The information bottleneck frames message learning as balancing observation-related referential information against coordination-related ordinal information.The setup combines a Lewis game and the information bottleneck [21].Source: §3.1 · p. 3
- Communication is represented as a changing graph of agents and message edges, with sparsity measured by the total message edges over an episode.Gating is a special form of targeting in which an agent vertex is disconnected from the graph.Source: §3.1 · p. 3
- The sparsity objective first maximizes task performance, then reduces communication while keeping performance fixed; its minimum feasible budget is the lossless sparse budget.The paper defines lossless sparse communication as satisfying this objective.Source: §3.1 · p. 4
- A single Lagrangian objective can permit suboptimal sparsity, trading off task performance and messaging constraints even when lossless sparsity is possible.The paper associates this mismatch with high variance in prior lossless-sparsity work.Source: §3.1 · p. 4
- The proposed strategy learns messages with independent referential information, then removes tokens with no ordinal utility to reach a lower lossless budget.This depends on first learning a communication policy that adequately solves the task.Source: §3.1 · p. 4
4 Proposed Methodology
IMGS-MAC combines an information-maximizing autoencoder with individualized communication regularization to learn informative messages and enforce sparse budgets. It first identifies messages that can be removed without additional reinforcement-learning training, then uses limited fine-tuning for budgets below the lossless range.
4 Proposed Methodology
- Individualized regularization combines an autoencoder that stabilizes dual policy-communication training with a per-agent communication penalty for fixed budgets.The authors state that individualized regularization is needed for the gradient signal to be adequately recognized.Source: §4 · p. 4
4.1 Sparsity through Information Maximization
- The communication module encodes messages, and each agent decodes its own hidden state plus other agents’ messages to reconstruct the concatenated team state.The reconstruction loss measures similarity between the latent communication and the concatenated state of all agents.Source: §4.1 · p. 5
- Messages are treated as null when removing them causes no significant reward change, measured within ϵ ≈ 1e−3.A null communication vector provides no information to another agent, expressed as I(mi; yj) = 0.Source: §4.1 · p. 5
- Combining null-message removal with structured latent representations can remove unnecessary communication without reducing performance, requiring no additional reinforcement-learning training.The paper calls enforcing sparsity from a non-sparse model without further reinforcement-learning training “zero-shot sparsity.”Source: §4.1 · p. 5
4.2 Sparsity through Individualized Regularization
- The communication budget b is a fraction of total agent-time opportunities, while m_AVG is the actual messaging fraction; the penalty applies when b < m_AVG < b*.For overconstrained budgets b < b*, limited additional MARL training with the regularization penalty is used to satisfy sparse constraints, which the paper calls few-shot sparsity.Source: §4.2 · p. 6
5 Experiments
Across the experiments, IMGS-MAC learns informative messages and identifies budgets that preserve non-sparse performance, while few-shot finetuning helps meet tighter budgets. The analyses compare message representations, training stability, and performance across continuous and discrete communication settings.
5 Experiments
- The experiments compare IMGS-MAC with IC3Net, analyze message representations and sparsity, and evaluate both continuous and discrete communication.The analyses include few-shot finetuning, zero-shot budget selection, and performance under lossless and tighter-than-lossless budgets.Source: §5 · p. 6
5.1 Information Maximization Analysis
- IMGS-MAC converges with lower variance than IC3Net and outperforms it in hard and discrete settings; its messages use fewer null vectors and have lower mutual information.The analysis links more independent messages to lower lossless budgets.Source: §5.1 · pp. 6–7, Figure 2 · p. 7, Table 1 · p. 7
5.2.1 Few-shot Sparsity
- Pretraining with an open gate at b=1, then finetuning at b<b*, reaches budgeted task-success convergence in about half the epochs of tri-objective training.Sparsity finetuning takes less than 10% of total training epochs, and the model follows the b=0.7 budget after a few dozen epochs.Source: §5.2.1 · pp. 7–8, Figure 3 · p. 8, Figure 4 · p. 8
5.2.2 Zero-shot Sparsity
- At b=b*, IMGS-MAC preserves non-sparse performance while reducing communication by 20–60%; below b*, task performance trades off as the budget shrinks.The lossless budget is specific to each learned communication model, and the reported lossless sparsity requires no additional training.Source: §5.2.2 · pp. 8–9, Table 2 · p. 8, Figure 5 · p. 9
6 Conclusion and Future Work
The conclusion frames individualized sparse communication as representation learning and reports lossless zero-shot sparsity plus individualized regularization for few-shot sparsity. It identifies residual token redundancy and nonzero mutual information as limitations, with stronger bounds and combined gating/compression proposed for future work.
Conclusion
- Lossless zero-shot sparsity can be achieved by training a communication-action policy with an autoencoder and analyzing non-sparse messaging, without additional reinforcement-learning training.Individualized regularization is used to limit performance loss under few-shot sparsity and meet over-constrained bandwidth scenarios.Source: §6 · p. 9
- The paper reframes individualized sparse communication as representation learning through the information bottleneck.Source: §6 · p. 9
Limitations
- Removing null messages can leave other removable messages undiscovered, while mutual information between tokens remains nonzero.Source: §6 · p. 9
Future Work
- Future work proposes stronger bounds on message-content independence and combining gating/targeting sparsity with communication compression.The authors also propose improving unsupervised representation learning to support sparser performance.Source: §6 · p. 9
A Experimental Setup
The experiments use blind traffic-junction and predator-prey environments, and report that IMGS-MAC reaches a minimum sparse budget with lossless performance in predator-prey. Training first uses an autoencoder, followed by budget analysis and, when needed, message-penalty fine-tuning.
Experimental Setup
- The experiments use 10 random seeds per variant, with 5000 samples per epoch, and RMSProp at learning rate 0.003.Source: A Experimental Setup · p. 12
- Traffic-junction agents navigate a narrow intersection without observing other agents, making informative communication necessary to avoid collisions.Communication and action occur in a single time-step; results are reported for the easiest and hardest variants that converge under continuous and discrete communication.Source: A Experimental Setup · p. 12
- Predators observe only their current and adjacent cells and succeed when all reach the prey before the episode limit.Source: A Experimental Setup · p. 12
- IMGS-MAC decreases predator-prey messaging to a minimum sparse budget b* with lossless performance.Source: A Experimental Setup · p. 12
- Training uses an autoencoder, tests for lossless budget adherence, then fine-tunes with a message penalty if needed.Source: A Experimental Setup · p. 12