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Found 4 papers, 3 papers with code
Robust Multi-Agent Reinforcement Learning with State Uncertainty
Then we propose a robust multi-agent Q-learning (RMAQ) algorithm to find such an equilibrium, with convergence guarantees.
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation
MOT-CUP demonstrates the importance of uncertainty quantification in both COD and MOT, and provides the first attempt to improve the accuracy and reduce the uncertainty in MOT based on COD through uncertainty propagation.
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?
Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed with the assumption that agents' policies are based on accurate state information.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Uncertainty Quantification of Collaborative Detection for Self-Driving
Our work is the first to estimate the uncertainty of collaborative object detection.
