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 1201–1210 of 1718 papers

TitleStatusHype
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning—0
Trust Region Policy Optimisation in Multi-Agent Reinforcement LearningCode1
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning—0
Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures—0
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain—0
DSDF: An approach to handle stochastic agents in collaborative multi-agent reinforcement learning—0
On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)—0
On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games—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