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 851–875 of 1718 papers

TitleStatusHype
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth—0
Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization—0
Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks—0
Mediated Multi-Agent Reinforcement LearningCode0
Hierarchical Task Network Planning for Facilitating Cooperative Multi-Agent Reinforcement Learning—0
Provably Learning Nash Policies in Constrained Markov Potential Games—0
Data Poisoning to Fake a Nash Equilibrium in Markov Games—0
A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications—0
Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agents—0
Negotiated Reasoning: On Provably Addressing Relative Over-Generalization—0
Inductive Bias for Emergent Communication in a Continuous Setting—0
A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement LearningCode0
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive AdvantagesCode0
Multi-Robot Path Planning Combining Heuristics and Multi-Agent Reinforcement Learning—0
Improving the generalizability and robustness of large-scale traffic signal control—0
Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement LearningCode0
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning—0
Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility—0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
Attention Schema in Neural Agents—0
Reinforcement Learning With Reward Machines in Stochastic Games—0
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities—0
A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem—0
Understanding the World to Solve Social Dilemmas Using 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