Search Results for author:
Found 8 papers, 3 papers with code
Are Large Language Models Chameleons?
Do large language models (LLMs) have their own worldviews and personality tendencies?
Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments
In contrast to existing results, we demonstrate that both FedSVRPG-M and FedHAPG-M, both of which leverage momentum mechanisms, can exactly converge to a stationary point of the average performance function, regardless of the magnitude of environment heterogeneity.
Constrained Reinforcement Learning Under Model Mismatch
Existing studies on constrained reinforcement learning (RL) may obtain a well-performing policy in the training environment.
Robust Electric Vehicle Balancing of Autonomous Mobility-On-Demand System: A Multi-Agent Reinforcement Learning Approach
In this work, we design a multi-agent reinforcement learning (MARL)-based framework for EAVs balancing in E-AMoD systems, with adversarial agents to model both the EAVs supply and mobility demand uncertainties that may undermine the vehicle balancing solutions.
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.
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
A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems
In this work, we design a robust and constrained multi-agent reinforcement learning (MARL) framework with state transition kernel uncertainty for EV AMoD systems.
Uncertainty Quantification of Collaborative Detection for Self-Driving
Our work is the first to estimate the uncertainty of collaborative object detection.
