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Viktor Prasanna

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5 papers

1 author row

AAMAS Conference 2026 Conference Paper

Deep Meta Coordination Graphs for Multi-Agent Reinforcement Learning

Nikunj Gupta
James Zachary Hare
Jesse Milzman
Rajgopal Kannan
Viktor Prasanna

This paper presents deep meta coordination graphs (DMCG) for learning cooperative policies in multi-agent reinforcement learning (MARL). Coordinationgraphformulationsencodelocalinteractions and accordingly factorize the joint value function of all agents to improve efficiency in MARL. DMCG learns a more expressive representation of agent interactions and use them to integrate agent information through graph convolutional networks. The goal is to enable an evolving coordination graph to guide effective coordination in cooperative MARL tasks. The graphs are jointly optimized withagents’valuefunctionstolearntoimplicitlyreasonaboutjoint actions, facilitating the end-to-end learning of interaction representations and coordinated policies. We demonstrate that DMCG consistently achieves state-of-the-art coordination performance and sample efficiency on challenging cooperative tasks, outperforming several prior graph-based and non-graph-based MARL baselines. Through several ablations, we also isolate the impact of individual components in DMCG, showing that the observed improvements are due to the meaningful design choices in this approach. All codes can be found here: https: //github. com/Nikunj-Gupta/dmcg-marl.

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NeurIPS Conference 2025 Conference Paper

Mixture of Scope Experts at Test: Generalizing Deeper Graph Neural Networks with Shallow Variants

Gangda Deng
Hongkuan Zhou
Rajgopal Kannan
Viktor Prasanna

Heterophilous graphs, where dissimilar nodes tend to connect, pose a challenge for graph neural networks (GNNs). Increasing the GNN depth can expand the scope (i. e. , receptive field), potentially finding homophily from the higher-order neighborhoods. However, GNNs suffer from performance degradation as depth increases. Despite having better expressivity, state-of-the-art deeper GNNs achieve only marginal improvements compared to their shallow variants. Through theoretical and empirical analysis, we systematically demonstrate a shift in GNN generalization preferences across nodes with different homophily levels as depth increases. This creates a disparity in generalization patterns between GNN models with varying depth. Based on these findings, we propose to improve deeper GNN generalization while maintaining high expressivity by Mixture of scope experts at test (Moscat). Experimental results show that Moscat works flexibly with various GNN architectures across a wide range of datasets while significantly improving accuracy.

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AAMAS Conference 2022 Conference Paper

Intelligent Communication over Realistic Wireless Networks in Multi-Agent Cooperative Games

Diyi Hu
Chi Zhang
Viktor Prasanna
Bhaskar Krishnamachari

In MARL, communication among agents is essential to establish cooperation. Over the realistic wireless network, many factors can affect transmission reliability, especially considering that the wireless network condition varies with agents’ mobility. We propose a framework that improves the intelligence of communication over realistic wireless networks in two fundamental aspects: (1) When: Agents learn the timing of communication based on message importance and wireless channel condition. We further propose a communication lagging technique to make the training end-to-end differentiable. (2) What: Agents augment message contents with wireless network measurements. The messages improve both the game and communication actions of the agents. Experiments on a standard environment show that compared with state-of-the-art, our framework enables more intelligent collaboration and thus achieves significantly better game performance, convergence speed and communication efficiency.

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NeurIPS Conference 2021 Conference Paper

Decoupling the Depth and Scope of Graph Neural Networks

Hanqing Zeng
Muhan Zhang
Yinglong Xia
Ajitesh Srivastava
Andrey Malevich
Rajgopal Kannan
Viktor Prasanna
Long Jin

State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the graph and model sizes. On large graphs, increasing the model depth often means exponential expansion of the scope (i. e. , receptive field). Beyond just a few layers, two fundamental challenges emerge: 1. degraded expressivity due to oversmoothing, and 2. expensive computation due to neighborhood explosion. We propose a design principle to decouple the depth and scope of GNNs – to generate representation of a target entity (i. e. , a node or an edge), we first extract a localized subgraph as the bounded-size scope, and then apply a GNN of arbitrary depth on top of the subgraph. A properly extracted subgraph consists of a small number of critical neighbors, while excluding irrelevant ones. The GNN, no matter how deep it is, smooths the local neighborhood into informative representation rather than oversmoothing the global graph into “white noise”. Theoretically, decoupling improves the GNN expressive power from the perspectives of graph signal processing (GCN), function approximation (GraphSAGE) and topological learning (GIN). Empirically, on seven graphs (with up to 110M nodes) and six backbone GNN architectures, our design achieves significant accuracy improvement with orders of magnitude reduction in computation and hardware cost.

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AAAI Conference 2015 Conference Paper

Influence-Driven Model for Time Series Prediction from Partial Observations

Saima Aman
Charalampos Chelmis
Viktor Prasanna

Applications in sustainability domains such as in energy, transportation, and natural resource and environment monitoring, increasingly use sensors for collecting data and sending it back to centrally located processing nodes. While data can usually be collected by the sensors at a very high speed, in many cases, it can not be sent back to central nodes at a frequency that is required for fast and real-time modeling and decisionmaking. This may be due to physical limitations of the transmission networks, or due to consumers limiting frequent transmission of data from sensors located at their premises for security and privacy concerns. We propose a novel solution to the problem of making short term predictions in absence of real-time data from sensors. A key implication of our work is that by using real-time data from only a small subset of influential sensors, we are able to make predictions for all sensors. We evaluated our approach with a large real-world electricity consumption data collected from smart meters in Los Angeles and the results show that between prediction horizons of 2 to 8 hours, despite lack of real time data, our influence model outperforms the baseline model that uses real-time data. Also, when using partial real-time data from only ≈ 7% influential smart meters, we witness prediction error increase by only ≈ 0. 5% over the baseline, thus demonstrating the usefulness of our method for practical scenarios.

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