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 1301–1350 of 1718 papers

TitleStatusHype
Local Patch AutoAugment with Multi-Agent CollaborationCode1
Adversarial attacks in consensus-based multi-agent reinforcement learning—0
The AI Arena: A Framework for Distributed Multi-Agent Reinforcement LearningCode1
A multi-agent reinforcement learning model of reputation and cooperation in human groups—0
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation—0
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight DeliveryCode1
Efficient UAV Trajectory-Planning using Economic Reinforcement Learning—0
Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter ControlCode2
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent GamesCode1
Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction ReportCode1
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive EnvironmentsCode0
Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning—0
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition—0
Decentralized Deterministic Multi-Agent Reinforcement Learning—0
Strategic bidding in freight transport using deep reinforcement learning—0
Multi-Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics ScenesCode1
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-LearningCode1
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning—0
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents—0
Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement LearningCode1
Scaling Multi-Agent Reinforcement Learning with Selective Parameter SharingCode1
Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning—0
Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning—0
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning—0
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement LearningCode1
Neural Recursive Belief States in Multi-Agent Reinforcement Learning—0
Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis—0
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors—0
Multi-Agent Reinforcement Learning with Temporal Logic SpecificationsCode1
Hybrid Information-driven Multi-agent Reinforcement Learning—0
Safe Multi-Agent Reinforcement Learning via Shielding—0
Data sharing gamesCode0
Fast Sequence Generation with Multi-Agent Reinforcement Learning—0
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with TransformersCode1
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned MessagingCode0
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning—0
Solving Common-Payoff Games with Approximate Policy IterationCode0
Coding for Distributed Multi-Agent Reinforcement Learning—0
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity—0
MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal ControlCode1
Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels—0
Multi-Agent Reinforcement Learning Based Resource Management in MEC- and UAV-Assisted Vehicular Networks—0
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning—0
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers—0
Multi-Agent Trust Region LearningCode1
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients—0
Adaptive Learning Rates for Multi-Agent Reinforcement Learning—0
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium—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