SOTAVerified

Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 651–700 of 1718 papers

TitleStatusHype
Finite-Time Analysis of Fully Decentralized Single-Timescale Actor-Critic—0
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning—0
AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning—0
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning—0
Goals are Enough: Inducing AdHoc cooperation among unseen Multi-Agent systems in IMFs—0
Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach—0
Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games—0
Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility—0
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters—0
Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents—0
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation—0
Graph Exploration for Effective Multi-agent Q-Learning—0
Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games—0
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management—0
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model—0
Cooperative Actor-Critic via TD Error Aggregation—0
GTDE: Grouped Training with Decentralized Execution for Multi-agent Actor-Critic—0
H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic—0
Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation—0
Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents—0
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games—0
Harmonia: A Multi-Agent Reinforcement Learning Approach to Data Placement and Migration in Hybrid Storage Systems—0
CORD: Generalizable Cooperation via Role Diversity—0
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism—0
Correcting Experience Replay for Multi-Agent Communication—0
Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL—0
Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization—0
Finite Horizon Multi-Agent Reinforcement Learning in Solving Optimal Control of State-Dependent Switched Systems—0
Attention-Driven Multi-Agent Reinforcement Learning: Enhancing Decisions with Expertise-Informed Tasks—0
Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration—0
Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs—0
Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication—0
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning—0
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks—0
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
Attention Schema in Neural Agents—0
Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Bidding—0
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play—0
Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks—0
Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense—0
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning—0
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning—0
AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learning—0
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning—0
Improving the generalizability and robustness of large-scale traffic signal control—0
Incorporating Pragmatic Reasoning Communication into Emergent Language—0
Homeostatic Coupling for Prosocial Behavior—0
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified