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 451–500 of 1718 papers

TitleStatusHype
Multi-Agent Reinforcement Learning with a Hierarchy of Reward Machines—0
Reaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination—0
PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement LearningCode0
Feint Behaviors and Strategies: Formalization, Implementation and Evaluation—0
SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition—0
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement LearningCode2
Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement LearningCode0
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning—0
Independent Learning in Constrained Markov Potential GamesCode0
Reinforcement Learning Based Robust Volt/Var Control in Active Distribution Networks With Imprecisely Known Delay—0
Shapley Value Based Multi-Agent Reinforcement Learning: Theory, Method and Its Application to Energy Network—0
A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making—0
Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning—0
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling—0
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks—0
Modelling crypto markets by multi-agent reinforcement learningCode0
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation—0
Conservative and Risk-Aware Offline Multi-Agent Reinforcement LearningCode0
Understanding Model Selection For Learning In Strategic Environments—0
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation—0
Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative Markov GamesCode0
Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message PassingCode1
Multimodal Query Suggestion with Multi-Agent Reinforcement Learning from Human Feedback—0
SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning SystemsCode0
Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs—0
Multi-agent Reinforcement Learning for Energy Saving in Multi-Cell Massive MIMO Systems—0
Settling Decentralized Multi-Agent Coordinated Exploration by Novelty SharingCode1
O(T^-1) Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games—0
Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints—0
Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning—0
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk ManagementCode2
FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game—0
Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning—0
Nash Soft Actor-Critic LEO Satellite Handover Management Algorithm for Flying Vehicles—0
Graph Attention-based Reinforcement Learning for Trajectory Design and Resource Assignment in Multi-UAV Assisted Communication—0
Autonomous Vehicle Patrolling Through Deep Reinforcement Learning: Learning to Communicate and Cooperate—0
Fully Independent Communication in Multi-Agent Reinforcement LearningCode0
Peer-to-Peer Energy Trading of Solar and Energy Storage: A Networked Multiagent Reinforcement Learning Approach—0
Multi-Agent Diagnostics for Robustness via Illuminated Diversity—0
Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach—0
Emergent Communication Protocol Learning for Task Offloading in Industrial Internet of Things—0
Emergent Dominance Hierarchies in Reinforcement Learning AgentsCode0
Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance—0
Measuring Policy Distance for Multi-Agent Reinforcement LearningCode0
The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Maritime Operational Technology Cyber Security—0
REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes—0
AgentMixer: Multi-Agent Correlated Policy Factorization—0
Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning AlgorithmsCode0
UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution—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