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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 726–750 of 1718 papers

TitleStatusHype
CARSS: Cooperative Attention-guided Reinforcement Subpath Synthesis for Solving Traveling Salesman Problem—0
Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach—0
Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks—0
Sparse Mean Field Load Balancing in Large Localized Queueing Systems—0
Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamicsCode0
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning—0
Robust Communicative Multi-Agent Reinforcement Learning with Active Defense—0
Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning—0
Multi-agent Reinforcement Learning: A Comprehensive Survey—0
Adaptive parameter sharing for multi-agent reinforcement learning—0
Efficiently Quantifying Individual Agent Importance in Cooperative MARL—0
On Diagnostics for Understanding Agent Training Behaviour in Cooperative MARL—0
How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning—0
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning—0
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning—0
Privacy Preserving Multi-Agent Reinforcement Learning in Supply Chains—0
Finite Horizon Multi-Agent Reinforcement Learning in Solving Optimal Control of State-Dependent Switched Systems—0
A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Decentralized Inverter-based Voltage Control—0
MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment—0
BenchMARL: Benchmarking Multi-Agent Reinforcement Learning—0
Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence—0
TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering ProblemCode0
Generalisable Agents for Neural Network Optimisation—0
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play—0
Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs—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