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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 16261650 of 1718 papers

TitleStatusHype
Concurrent Meta Reinforcement LearningCode0
Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching System0
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning0
The StarCraft Multi-Agent ChallengeCode1
Whole-Chain Recommendations0
Reinforcement Learning from Hierarchical CriticsCode0
Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning0
Decentralized Multi-Agents by Imitation of a Centralized Controller0
Learning to Schedule Communication in Multi-agent Reinforcement LearningCode0
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?Code0
A Regulation Enforcement Solution for Multi-agent Reinforcement Learning0
Multi-Agent Reinforcement Learning with Multi-Step Generative Models0
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning0
Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive ReasoningCode0
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning0
Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies0
The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) CompetitionCode0
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning0
Multi-agent Reinforcement Learning Embedded Game for the Optimization of Building Energy Control and Power System Planning0
Optimizing Market Making using Multi-Agent Reinforcement Learning0
Malthusian Reinforcement Learning0
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning0
Communication-Efficient Policy Gradient Methods for Distributed Reinforcement Learning0
Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents0
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Benchmark Results

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