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
Multi-Agent Reinforcement Learning with a Hierarchy of Reward Machines—0
PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement LearningCode0
Reaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination—0
Feint Behaviors and Strategies: Formalization, Implementation and Evaluation—0
SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition—0
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement LearningCode2
Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement LearningCode0
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning—0
Independent Learning in Constrained Markov Potential GamesCode0
Reinforcement Learning Based Robust Volt/Var Control in Active Distribution Networks With Imprecisely Known Delay—0
Shapley Value Based Multi-Agent Reinforcement Learning: Theory, Method and Its Application to Energy Network—0
A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making—0
Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning—0
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling—0
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks—0
Modelling crypto markets by multi-agent reinforcement learningCode0
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation—0
Conservative and Risk-Aware Offline Multi-Agent Reinforcement LearningCode0
Understanding Model Selection For Learning In Strategic Environments—0
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation—0
Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative Markov GamesCode0
Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message PassingCode1
Multimodal Query Suggestion with Multi-Agent Reinforcement Learning from Human Feedback—0
SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning SystemsCode0
Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs—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