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

Showing 901910 of 1718 papers

TitleStatusHype
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning0
PIMAEX: Multi-Agent Exploration through Peer Incentivization0
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL0
Policy Diversity for Cooperative Agents0
Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response0
Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games0
Policy Optimization for Continuous-time Linear-Quadratic Graphon Mean Field Games0
Policy Optimization for Markov Games: Unified Framework and Faster Convergence0
Polymatrix Competitive Gradient Descent0
PooL: Pheromone-inspired Communication Framework forLarge Scale Multi-Agent Reinforcement Learning0
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Benchmark Results

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