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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 1551–1600 of 1718 papers

TitleStatusHype
SMIX(λ): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement LearningCode0
Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation—0
PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement LearningCode1
Deep Decentralized Reinforcement Learning for Cooperative Control—0
Convergent Policy Optimization for Safe Reinforcement LearningCode0
MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding—0
A New Framework for Multi-Agent Reinforcement Learning -- Centralized Training and Exploration with Decentralized Execution via Policy Distillation—0
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement LearningCode0
MAVEN: Multi-Agent Variational ExplorationCode0
RLCard: A Toolkit for Reinforcement Learning in Card GamesCode0
Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching—0
Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems—0
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized CriticsCode0
Deep Coordination GraphsCode0
Multi-Agent Actor-Critic with Hierarchical Graph Attention Network—0
Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals—0
Multi-Agent Hierarchical Reinforcement Learning for Humanoid Navigation—0
Modeling Fake News in Social Networks with Deep Multi-Agent Reinforcement Learning—0
Probabilistic View of Multi-agent Reinforcement Learning: A Unified Approach—0
Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization—0
Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-Net—0
Stock market microstructure inference via multi-agent reinforcement learning—0
Modeling Sensorimotor Coordination as Multi-Agent Reinforcement Learning with Differentiable Communication—0
On Memory Mechanism in Multi-Agent Reinforcement Learning—0
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning—0
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based ControlCode0
STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control—0
Universal Policies to Learn Them AllCode0
Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning—0
Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking—0
A Review of Cooperative Multi-Agent Deep Reinforcement Learning—0
Large-Scale Traffic Signal Control Using a Novel Multi-Agent Reinforcement Learning—0
Fast Multi-Agent Temporal-Difference Learning via Homotopy Stochastic Primal-Dual Optimization—0
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning—0
Health-Informed Policy Gradients for Multi-Agent Reinforcement LearningCode0
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition—0
Finite-Time Performance of Distributed Temporal Difference Learning with Linear Function Approximation—0
Arena: a toolkit for Multi-Agent Reinforcement LearningCode0
Prioritized Guidance for Efficient Multi-Agent Reinforcement Learning Exploration—0
Shapley Q-value: A Local Reward Approach to Solve Global Reward GamesCode0
Voting-Based Multi-Agent Reinforcement Learning for Intelligent IoT—0
Multiple Landmark Detection using Multi-Agent Reinforcement LearningCode0
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning—0
Escaping the State of Nature: A Hobbesian Approach to Cooperation in Multi-agent Reinforcement Learning—0
Finding Friend and Foe in Multi-Agent GamesCode0
Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning—0
Learning Transferable Cooperative Behavior in Multi-Agent TeamsCode0
Options as responses: Grounding behavioural hierarchies in multi-agent RL—0
Attentional Policies for Cross-Context Multi-Agent Reinforcement Learning—0
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
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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