5 papers
Stationary Robust Mean-Field Games under Model Mismatches
Yue Wang
Deploying multi-agent reinforcement learning (MARL) in the real world is often limited by model mismatches between the training simulators and the true environment, which could be…
Robust Transfer Learning with Side Information
Akram S. Awad, Shihab Ahmed, Yue Wang +1
Robust Markov Decision Processes (MDPs) address environmental shift through distributionally robust optimization (DRO) by finding an optimal worst-case policy within an uncertainty…
Online Robust Reinforcement Learning with General Function Approximation
Debamita Ghosh, George K. Atia, Yue Wang
In many real-world settings, reinforcement learning systems suffer performance degradation when the environment encountered at deployment differs from that observed during training…
Sample-Efficient Distributionally Robust Multi-Agent Reinforcement Learning via Online Interaction
Zain Ulabedeen Farhat, Debamita Ghosh, George K. Atia +1
Well-trained multi-agent systems can fail when deployed in real-world environments due to model mismatches between the training and deployment environments, caused by environment u…
ORVIT: Near-Optimal Online Distributionally Robust Reinforcement Learning
Debamita Ghosh, George K. Atia, Yue Wang
We investigate reinforcement learning (RL) in the presence of distributional mismatch between training and deployment, where policies trained in simulators often underperform in pr…