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 10261050 of 1718 papers

TitleStatusHype
Distributed Reinforcement Learning for Robot Teams: A Review0
Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction GamesCode0
RL4ReAl: Reinforcement Learning for Register Allocation0
Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning0
Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach0
Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning0
Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning0
Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit DesignCode1
Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects0
Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty0
A Survey of Multi-Agent Deep Reinforcement Learning with Communication0
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed AgentsCode0
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information CollaborationCode1
Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning0
An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility0
The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation0
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective0
Impression Allocation and Policy Search in Display Advertising0
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework0
On-the-fly Strategy Adaptation for ad-hoc Agent Coordination0
Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer EmpowermentCode1
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network0
Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility0
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Benchmark Results

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