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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 701–750 of 1718 papers

TitleStatusHype
Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning—0
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
Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance—0
Emergent Dominance Hierarchies in Reinforcement Learning AgentsCode0
Measuring Policy Distance for Multi-Agent Reinforcement LearningCode0
Multi-Agent Reinforcement Learning for Maritime Operational Technology Cyber Security—0
The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learning—0
AgentMixer: Multi-Agent Correlated Policy Factorization—0
REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes—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
Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey—0
A Tensor Network Implementation of Multi Agent Reinforcement Learning—0
ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering—0
Adaptive trajectory-constrained exploration strategy for deep reinforcement learningCode0
Exploiting hidden structures in non-convex games for convergence to Nash equilibrium—0
CARSS: Cooperative Attention-guided Reinforcement Subpath Synthesis for Solving Traveling Salesman Problem—0
Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach—0
Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks—0
Sparse Mean Field Load Balancing in Large Localized Queueing Systems—0
Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamicsCode0
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning—0
Robust Communicative Multi-Agent Reinforcement Learning with Active Defense—0
Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning—0
Multi-agent Reinforcement Learning: A Comprehensive Survey—0
Adaptive parameter sharing for multi-agent reinforcement learning—0
Efficiently Quantifying Individual Agent Importance in Cooperative MARL—0
On Diagnostics for Understanding Agent Training Behaviour in Cooperative MARL—0
How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning—0
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning—0
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning—0
Privacy Preserving Multi-Agent Reinforcement Learning in Supply Chains—0
Finite Horizon Multi-Agent Reinforcement Learning in Solving Optimal Control of State-Dependent Switched Systems—0
A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Decentralized Inverter-based Voltage Control—0
MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment—0
BenchMARL: Benchmarking Multi-Agent Reinforcement Learning—0
Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence—0
TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering ProblemCode0
Generalisable Agents for Neural Network Optimisation—0
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play—0
Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs—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