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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 1626–1650 of 1718 papers

TitleStatusHype
Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning—0
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach—0
M^3RL: Mind-aware Multi-agent Management Reinforcement Learning—0
Intrinsic Social Motivation via Causal Influence in Multi-Agent RL—0
Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning—0
SSoC: Learning Spontaneous and Self-Organizing Communication for Multi-Agent Collaboration—0
Inducing Cooperation via Learning to reshape rewards in semi-cooperative multi-agent reinforcement learning—0
Argus: Smartphone-enabled Human Cooperation via Multi-Agent Reinforcement Learning for Disaster Situational Awareness—0
Teaching on a Budget in Multi-Agent Deep Reinforcement Learning—0
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning—0
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios—0
Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach—0
Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading—0
Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving—0
Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus—0
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces—0
Concurrent Meta Reinforcement LearningCode0
Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching System—0
Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning—0
Whole-Chain Recommendations—0
Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning—0
Reinforcement Learning from Hierarchical CriticsCode0
Decentralized Multi-Agents by Imitation of a Centralized Controller—0
Learning to Schedule Communication in Multi-agent Reinforcement LearningCode0
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?Code0
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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