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 1376–1400 of 1718 papers

TitleStatusHype
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