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 576–600 of 1718 papers

TitleStatusHype
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection—0
A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives—0
Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping—0
Distributional Black-Box Model Inversion Attack with Multi-Agent Reinforcement Learning—0
Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Games—0
Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning—0
Distributed Value Decomposition Networks with Networked Agents—0
CCL: Collaborative Curriculum Learning for Sparse-Reward Multi-Agent Reinforcement Learning via Co-evolutionary Task Evolution—0
Causal Multi-Agent Reinforcement Learning: Review and Open Problems—0
Adversarial attacks in consensus-based multi-agent reinforcement learning—0
Distributed Traffic Control in Complex Dynamic Roadblocks: A Multi-Agent Deep RL Approach—0
Causal Mean Field Multi-Agent Reinforcement Learning—0
Distributed Reinforcement Learning for Robot Teams: A Review—0
Causality Detection for Efficient Multi-Agent Reinforcement Learning—0
A New Policy Iteration Algorithm For Reinforcement Learning in Zero-Sum Markov Games—0
Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach—0
Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies—0
CARSS: Cooperative Attention-guided Reinforcement Subpath Synthesis for Solving Traveling Salesman Problem—0
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning—0
Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus—0
A New Framework for Multi-Agent Reinforcement Learning -- Centralized Training and Exploration with Decentralized Execution via Policy Distillation—0
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning—0
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition—0
Ego-centric Learning of Communicative World Models for Autonomous Driving—0
Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning—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