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 701–750 of 1718 papers

TitleStatusHype
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances—0
Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization—0
A Multi-Agent Reinforcement Learning Framework for Evaluating the U.S. Ending the HIV Epidemic Plan—0
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning—0
Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks—0
Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology—0
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines—0
Decentralized Deterministic Multi-Agent Reinforcement Learning—0
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation—0
A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs—0
Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control—0
Inducing Stackelberg Equilibrium through Spatio-Temporal Sequential Decision-Making in Multi-Agent Reinforcement Learning—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
Inducing Cooperative behaviour in Sequential-Social dilemmas through Multi-Agent Reinforcement Learning using Status-Quo Loss—0
Individual specialization in multi-task environments with multiagent reinforcement learners—0
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning—0
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration—0
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
AdaptNet: Rethinking Sensing and Communication for a Seamless Internet of Drones Experience—0
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence—0
Decentralized Adaptive Formation via Consensus-Oriented Multi-Agent Communication—0
Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning—0
Independent and Decentralized Learning in Markov Potential Games—0
Incorporating Pragmatic Reasoning Communication into Emergent Language—0
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition—0
Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games—0
Improving the generalizability and robustness of large-scale traffic signal control—0
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning—0
B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning—0
Improving International Climate Policy via Mutually Conditional Binding Commitments—0
Group-Agent Reinforcement Learning—0
Independent Natural Policy Gradient Always Converges in Markov Potential Games—0
Improving Global Parameter-sharing in Physically Heterogeneous Multi-agent Reinforcement Learning with Unified Action Space—0
Independent Policy Mirror Descent for Markov Potential Games: Scaling to Large Number of Players—0
Improved cooperation by balancing exploration and exploitation in intertemporal social dilemma tasks—0
DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Training—0
Impression Allocation and Policy Search in Display Advertising—0
Inducing Cooperation via Learning to reshape rewards in semi-cooperative multi-agent reinforcement learning—0
Implementations that Matter in Cooperative Multi-Agent Reinforcement Learning—0
Inductive Bias for Emergent Communication in a Continuous Setting—0
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning—0
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning—0
Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents—0
Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning—0
A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem—0
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning—0
Information Structure in Mappings: An Approach to Learning, Representation, and Generalisation—0
Metric Policy Representations for Opponent Modeling—0
Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization—0
Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using 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