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 951–1000 of 1718 papers

TitleStatusHype
Group-Agent Reinforcement Learning—0
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning—0
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition—0
Decentralized Adaptive Formation via Consensus-Oriented Multi-Agent Communication—0
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration—0
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control—0
Decentralized Deterministic Multi-Agent Reinforcement Learning—0
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines—0
Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks—0
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning—0
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances—0
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty—0
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method—0
Decentralized Multi-Agent Reinforcement Learning with Global State Prediction—0
Decentralized Multi-agent Reinforcement Learning based State-of-Charge Balancing Strategy for Distributed Energy Storage System—0
Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network—0
Decentralized Policy Optimization—0
Decentralized Q-Learning in Zero-sum Markov Games—0
Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements—0
Decentralized scheduling through an adaptive, trading-based multi-agent system—0
Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network—0
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information—0
Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective—0
Deconstructing Cooperation and Ostracism via Multi-Agent Reinforcement Learning—0
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability—0
DeepHive: A multi-agent reinforcement learning approach for automated discovery of swarm-based optimization policies—0
Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks—0
Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing—0
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces—0
Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms—0
Reinforcement Learning for Freeway Lane-Change Regulation via Connected Vehicles—0
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions—0
Reinforcement Learning in Non-Stationary Discrete-Time Linear-Quadratic Mean-Field Games—0
Reinforcement Learning on Dyads to Enhance Medication Adherence—0
Reinforcement Learning With Reward Machines in Stochastic Games—0
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning—0
REMAX: Relational Representation for Multi-Agent Exploration—0
Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning—0
Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning—0
Replication of Multi-agent Reinforcement Learning for the "Hide and Seek" Problem—0
Representation Learning For Efficient Deep Multi-Agent Reinforcement Learning—0
Residual Q-Networks for Value Function Factorizing in Multi-Agent Reinforcement Learning—0
Resilient robot teams: a review integrating decentralised control, change-detection, and learning—0
Understanding Model Selection For Learning In Strategic Environments—0
REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes—0
Revealing Robust Oil and Gas Company Macro-Strategies using Deep Multi-Agent Reinforcement Learning—0
Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective—0
Revisiting Some Common Practices in Cooperative 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