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 1051–1075 of 1718 papers

TitleStatusHype
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning—0
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model—0
Can Mean Field Control (MFC) Approximate Cooperative Multi Agent Reinforcement Learning (MARL) with Non-Uniform Interaction?Code0
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions—0
Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object TrackingCode2
A Decentralized Communication Framework based on Dual-Level Recurrence for Multi-Agent Reinforcement Learning—0
MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement LearningCode0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
Cooperative Artificial IntelligenceCode0
PooL: Pheromone-inspired Communication Framework forLarge Scale Multi-Agent Reinforcement Learning—0
Shaping Advice in Deep Reinforcement LearningCode0
Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games—0
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
Motivating Physical Activity via Competitive Human-Robot Interaction—0
The Shapley Value in Machine LearningCode1
Group-Agent Reinforcement Learning—0
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning—0
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence—0
Multi-Agent Path Finding with Prioritized Communication LearningCode1
Attacking c-MARL More Effectively: A Data Driven Approach—0
Trust Region Bounds for Decentralized PPO Under Non-stationarity—0
Generalization in Cooperative Multi-Agent Systems—0
Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center—0
Probe-Based Interventions for Modifying Agent Behavior—0
Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning—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