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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

Showing 16411650 of 1718 papers

TitleStatusHype
Few is More: Task-Efficient Skill-Discovery for Multi-Task Offline Multi-Agent Reinforcement Learning0
Few-Shot Teamwork0
Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games0
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning0
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning0
Finite Horizon Multi-Agent Reinforcement Learning in Solving Optimal Control of State-Dependent Switched Systems0
Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents0
Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation0
Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games0
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified