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
Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning—0
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?—0
When Is Diversity Rewarded in Cooperative Multi-Agent Learning?—0
When is Offline Two-Player Zero-Sum Markov Game Solvable?—0
Who is Helping Whom? Analyzing Inter-dependencies to Evaluate Cooperation in Human-AI Teaming—0
O(T^-1) Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games—0
Wildfire Autonomous Response and Prediction Using Cellular Automata (WARP-CA)—0
Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning—0
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping—0
Model based Multi-agent Reinforcement Learning with Tensor Decompositions—0
Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects—0
Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-level—0
Whole-Chain Recommendations—0
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games—0
Modeling Fake News in Social Networks with Deep Multi-Agent Reinforcement Learning—0
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance—0
Modeling Others using Oneself in Multi-Agent Reinforcement Learning—0
Modeling Sensorimotor Coordination as Multi-Agent Reinforcement Learning with Differentiable Communication—0
Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning—0
Trust Region Bounds for Decentralized PPO Under Non-stationarity—0
Motivating Physical Activity via Competitive Human-Robot Interaction—0
M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions—0
Multi-Agent Actor-Critic with Generative Cooperative Policy Network—0
Multi-Agent Actor-Critic with Hierarchical Graph Attention Network—0
Multi-agent Actor-Critic with Time Dynamical Opponent Model—0
Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach—0
Multi-Agent Automated Machine Learning—0
CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning—0
Multi-agent Continual Coordination via Progressive Task Contextualization—0
Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search—0
Multi-agent Databases via Independent Learning—0
Multi-agent Deep Covering Skill Discovery—0
Energy Management of Multi-mode Hybrid Electric Vehicles based on Hand-shaking Multi-agent Learning—0
Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem—0
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations—0
Multi-Agent Deep Reinforcement Learning with Adaptive Policies—0
Multi-Agent Diagnostics for Robustness via Illuminated Diversity—0
Multi-Agent Game Abstraction via Graph Attention Neural Network—0
Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance—0
Multi-Agent Hierarchical Reinforcement Learning for Humanoid Navigation—0
Multi-Agent Hybrid SAC for Joint SS-DSA in CRNs—0
Multi-Agent Informational Learning Processes—0
Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation—0
Multi-agent Natural Actor-critic Reinforcement Learning Algorithms—0
Multi-Agent Neural Rewriter for Vehicle Routing with Limited Disclosure of Costs—0
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments—0
An Energy-aware and Fault-tolerant Deep Reinforcement Learning based approach for Multi-agent Patrolling Problems—0
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation—0
Multi-agent Policy Reciprocity with Theoretical Guarantee—0
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem—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