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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

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Showing 1201–1225 of 1718 papers

TitleStatusHype
Who is Helping Whom? Analyzing Inter-dependencies to Evaluate Cooperation in Human-AI Teaming—0
O(T^-1) Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games—0
Wildfire Autonomous Response and Prediction Using Cellular Automata (WARP-CA)—0
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning—0
Deep Decentralized Reinforcement Learning for Cooperative Control—0
A Survey on Self-play Methods in Reinforcement Learning—0
Environment Complexity and Nash Equilibria in a Sequential Social Dilemma—0
Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning—0
Factorized Deep Q-Network for Cooperative Multi-Agent Reinforcement Learning in Victim Tagging—0
Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization—0
SrSv: Integrating Sequential Rollouts with Sequential Value Estimation for Multi-agent Reinforcement Learning—0
Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors—0
Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics—0
Signal attenuation enables scalable decentralized multi-agent reinforcement learning over networks—0
Ego-centric Learning of Communicative World Models for Autonomous Driving—0
A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems—0
A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs—0
A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning—0
Abstracting Geo-specific Terrains to Scale Up Reinforcement Learning—0
AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning—0
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning—0
ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning—0
Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing—0
Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning—0
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition—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