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

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Showing 451–475 of 1718 papers

TitleStatusHype
Distributed Reinforcement Learning for Robot Teams: A Review—0
Causality Detection for Efficient Multi-Agent Reinforcement Learning—0
Decentralized Policy Optimization—0
Distributed Traffic Control in Complex Dynamic Roadblocks: A Multi-Agent Deep RL Approach—0
Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network—0
Causal Multi-Agent Reinforcement Learning: Review and Open Problems—0
Distributed Value Decomposition Networks with Networked Agents—0
Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning—0
A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles—0
Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping—0
Decentralized Multi-agent Reinforcement Learning based State-of-Charge Balancing Strategy for Distributed Energy Storage System—0
Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Games—0
Divergence-Regularized Multi-Agent Actor-Critic—0
Diverse Conventions for Human-AI Collaboration—0
Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility—0
DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach—0
Decentralized Multi-Agent Reinforcement Learning with Global State Prediction—0
Double Distillation Network for Multi-Agent Reinforcement Learning—0
A Multi-Agent Reinforcement Learning Framework for Evaluating the U.S. Ending the HIV Epidemic Plan—0
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection—0
Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks—0
DSDF: An approach to handle stochastic agents in collaborative multi-agent reinforcement learning—0
DSDF: Coordinated look-ahead strategy in stochastic multi-agent reinforcement learning—0
Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative Multi-Agent Reinforcement Learning—0
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method—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