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 1201–1250 of 1718 papers

TitleStatusHype
Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center—0
Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning—0
Probe-Based Interventions for Modifying Agent Behavior—0
Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized TeamingCode0
Anytime PSRO for Two-Player Zero-Sum Games—0
Interpretable Learned Emergent Communication for Human-Agent Teams—0
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive PrincipalCode0
Implementations that Matter in Cooperative Multi-Agent Reinforcement Learning—0
When is Offline Two-Player Zero-Sum Markov Game Solvable?—0
Distributed Cooperative Multi-Agent Reinforcement Learning with Directed Coordination Graph—0
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning—0
Offsetting Unequal Competition through RL-assisted Incentive Schemes—0
Deep Reinforcement Learning, a textbook—0
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning—0
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation—0
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning—0
Learning Cooperative Multi-Agent Policies with Partial Reward Decoupling—0
CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning—0
Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games—0
Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward—0
Cooperative Multi-Agent Reinforcement Learning with Hypergraph ConvolutionCode0
Self-Organized Polynomial-Time Coordination GraphsCode0
MDPGT: Momentum-based Decentralized Policy Gradient TrackingCode0
Reward-Free Attacks in Multi-Agent Reinforcement Learning—0
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RLCode0
RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents—0
Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning—0
The Power of Communication in a Distributed Multi-Agent System—0
Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning—0
Evaluating Generalization and Transfer Capacity of Multi-Agent Reinforcement Learning Across Variable Number of Agents—0
Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings—0
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning—0
Application of Multi-Agent Reinforcement Learning for Battery Management in Renewable Mini-Grids—0
Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS—0
Multi-lingual agents through multi-headed neural networksCode0
Off-Policy Correction For Multi-Agent Reinforcement LearningCode0
Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning—0
Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods—0
Polymatrix Competitive Gradient Descent—0
Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration—0
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning—0
Collaboration Promotes Group Resilience in Multi-Agent AI—0
Causal Multi-Agent Reinforcement Learning: Review and Open Problems—0
Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium controlCode0
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic—0
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement LearningCode0
Multi Agent Reinforcement Learning Trajectory Design and Two-Stage Resource Management in CoMP UAV VLC Networks—0
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel—0
A Supervised-Learning based Hour-Ahead Demand Response of a Behavior-based HEMS approximating MILP Optimization—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