Author name cluster

Gaurav Dixit

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

2 author rows

AAMAS Conference 2026 Conference Paper

Pareto-Guided Exploration for Multi-Objective Multiagent Learning

Gaurav Dixit
Kagan Tumer

Cooperativemulti-objectivemultiagentsettingsrequirediscovering teams that realize diverse trade-offs across conflicting objectives. In multi-objective reinforcement learning, policies are typically optimized via scalarization of expected returns, while Pareto-based methods approximate sets of non-dominated solutions in objective space. In cooperative settings, however, these perspectives are typically treated in isolation. We introduce Multiagent Pareto- Led Exploration (MAPLE), a framework that couples preferencealignedactor-criticlearningwithParetodominance-basedselection over expected team returns. MAPLE trains policies under multiple preference vectors while preserving and reusing agents based on their contribution to non-dominated teams through a shared archive. Empirical results in cooperative continuous-control benchmarks demonstrate improved Pareto front coverage and greater policy composability across trade-offs. These findings suggest that coupling scalarized learning with Pareto-level selection provides a principled mechanism for multi-objective multiagent learning.

PDF

ECAI Conference 2025 Conference Paper

Multiagent Quality-Diversity for Effective Adaptation

Siddarth Iyer
Ayhan Alp Aydeniz
Gaurav Dixit
Kagan Tumer

Robust adaptation in multiagent settings requires learning not just a single optimal behavior, but a repertoire of high-performing and diverse team behaviors that can succeed under environmental contingencies. Traditional multiagent reinforcement learning methods typically converge to a single specialized team behavior, limiting their adaptability. Recent approaches like Mix-ME promote behavioral diversity but rely solely on evolutionary operators, often resulting in sample-inefficiency and uncoordinated team composition. This work introduces Multiagent Sample-Efficient Quality-Diversity (MASQD), a learning framework that produces an archive of diverse, high-performing multiagent teams. MASQD builds on the Cross-Entropy Method Reinforcement Learning algorithm and extends it to the multiagent setting by representing teams as parameter-shared neural networks, directing exploration from previously discovered behaviors, and guiding refinement through a descriptor-conditioned critic. Through this coupling of anchored exploration and targeted exploitation, MASQD produces functional diversity: teams that are not only behaviorally distinct but also robust and effective under varied conditions. Experiments across four Multiagent MuJoCo tasks show that MASQD outperforms state-of-the-art baselines in both team fitness and functional diversity.

Details

AAMAS Conference 2024 Conference Paper

Influence-Focused Asymmetric Island Model

Andrew Festa
Gaurav Dixit
Kagan Tumer

Learning good joint-behaviors is challenging in multiagent settings due to the inherent non-stationarity: agents adapt their policies and act simultaneously. This is aggravated when the agents are asymmetric (agents have distinct capabilities and objectives) and must learn complementary behaviors required to work as a team. The Asymmetric Island Model partially addresses this by independently optimizing class-specific and team-wide behaviors. However, optimizing class-specific behaviors in isolation can produce egocentric behaviors that yield sub-optimal inter-class behaviors. This work introduces the Influence-Focused Asymmetric Island model (IF- AIM), a hierarchical framework that explicitly reinforces inter-class behaviors by optimizing class-specific behaviors conditioned on the expectation of behaviors of the complementary agent classes. An experiment in the harvest environment highlights the effectiveness of our method in optimizing adaptable inter-class behaviors.

PDF

ECAI Conference 2024 Conference Paper

Objective-Informed Diversity for Multi-Objective Multiagent Coordination

Gaurav Dixit
Kagan Tumer

To coordinate in multiagent settings characterized by multiple objectives, asymmetric agents (agents with distinct capabilities and preferences) must learn diverse behaviors to balance trade-offs between agent-specific and team objectives. Hierarchical methods partially address this by leveraging a combination of Quality-Diversity methods that illuminate the behavior space and evolutionary algorithms that use non-dominated sorting over the explored behaviors to improve coverage in the objective space. However, optimizing diverse behaviors and trade-offs in isolation is susceptible to producing egocentric behaviors that favor agent-specific objectives at the cost of team objectives. This work introduces the Multi-Objective Informed Island Model (MOI-IM), an asymmetric multiagent learning framework that fosters diverse behaviors and rich inter-agent relationships, necessary to balance potentially conflicting and misaligned objectives. An evolutionary algorithm improves coverage in the objective space by evolving a population of teams, while a gradient-based optimization infers and progressively explores the behavior space by fluidly adapting search to regions that produce policies with non-dominated trade-offs. The two processes are coupled via shared replay buffers to ensure alignment between coverage in the behavior and objective space. Empirical results on an asymmetric multi-objective coordination problem highlight MOI-IM’s ability to produce teams that can express diverse trade-offs and robust relationships required to balance misaligned objectives.

Details

AAMAS Conference 2023 Conference Paper

Learning Inter-Agent Synergies in Asymmetric Multiagent Systems

Gaurav Dixit
Kagan Tumer

In multiagent systems that require coordination, agents must learn diverse policies that enable them to achieve their individual and team objectives. Multiagent Quality-Diversity methods partially address this problem by filtering the joint space of policies to smaller sub-spaces that make the diversification of agent policies tractable. However, in teams of asymmetric agents (agents with different objectives and capabilities), the search for diversity is primarily driven by the need to find policies that will allow agents to assume complementary roles required to work together in teams. This work introduces Asymmetric Island Model (AIM), a multiagent framework that enables populations of asymmetric agents to learn diverse complementary policies that foster teamwork via dynamic population size allocation on a wide variety of team tasks. The key insight of AIM is that the competitive pressure arising from the distribution of policies on different team-wide tasks drives the agents to explore regions of the policy space that yield specializations that generalize across tasks. Simulation results on multiple variations of a remote habitat problem highlight the strength of AIM in discovering robust synergies that allow agents to operate near-optimally in response to the changing team composition and policies of other agents.

PDF

AAMAS Conference 2022 Conference Paper

Behavior Exploration and Team Balancing for Heterogeneous Multiagent Coordination

Gaurav Dixit
Kagan Tumer

Diversity in behaviors is instrumental for robust team performance in many multiagent tasks which require agents to coordinate. Unfortunately, exhaustive search through the agents’ behavior spaces is often intractable. This paper introduces Behavior Exploration for Heterogeneous Teams (BEHT), a multi-level learning framework that enables agents to progressively explore regions of the behavior space that promote team coordination on diverse goals. By combining diversity search to maximize agent-specific rewards and evolutionary optimization to maximize the team-based fitness, our method effectively filters regions of the behavior space that are conducive to agent coordination. We demonstrate the diverse behaviors and synergies that are method allows agents to learn on a multiagent exploration problem.

PDF