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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 1351–1400 of 1718 papers

TitleStatusHype
Transferable and Distributed User Association Policies for 5G and Beyond Networks—0
Decentralized Q-Learning in Zero-sum Markov Games—0
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning—0
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment—0
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning—0
MARL with General Utilities via Decentralized Shadow Reward Actor-Critic—0
KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning—0
From Motor Control to Team Play in Simulated Humanoid Football—0
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound—0
Dependent Multi-Task Learning with Causal Intervention for Image Captioning—0
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach—0
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning—0
Hierarchical RNNs-Based Transformers MADDPG for Mixed Cooperative-Competitive Environments—0
AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning—0
Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees—0
Scalable, Decentralized Multi-Agent Reinforcement Learning Methods Inspired by Stigmergy and Ant Colonies—0
Reducing Bus Bunching with Asynchronous Multi-Agent Reinforcement Learning—0
Mean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing—0
Discrete-Time Mean Field Control with Environment States—0
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients—0
Birds of a Feather Flock Together: A Close Look at Cooperation Emergence via Multi-Agent RL—0
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning—0
Agent-Centric Representations for Multi-Agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computing—0
Two-stage training algorithm for AI robot soccer—0
Towards Resilience for Multi-Agent QD-Learning—0
NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning—0
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges—0
Energy Efficient Edge Computing: When Lyapunov Meets Distributed Reinforcement Learning—0
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World—0
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport—0
Shaping Advice in Deep Multi-Agent Reinforcement LearningCode0
KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning—0
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication—0
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target PredictionCode0
Regularized Softmax Deep Multi-Agent Q-Learning—0
Learning to Robustly Negotiate Bi-Directional Lane Usage in High-Conflict Driving Scenarios—0
Adversarial attacks in consensus-based multi-agent reinforcement learning—0
A multi-agent reinforcement learning model of reputation and cooperation in human groups—0
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation—0
Efficient UAV Trajectory-Planning using Economic Reinforcement Learning—0
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning—0
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive EnvironmentsCode0
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition—0
Decentralized Deterministic Multi-Agent Reinforcement Learning—0
Strategic bidding in freight transport using deep reinforcement learning—0
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning—0
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents—0
Modeling the Interaction between Agents 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