10 papers
Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis
Zachary Roch, George Atia, Yue Wang
Robust reinforcement learning (RL) under the average-reward criterion is essential for long-term decision-making, particularly when the environment may differ from its training dyn…
LANTERN: LLM-Augmented Neurosymbolic Transfer with Experience-Gated Reasoning Networks
Mahyar Alinejad, Yue Wang, Amrit Singh Bedi +1
Transfer learning in reinforcement learning (RL) seeks to accelerate learning in new tasks by leveraging knowledge from related sources. Existing neurosymbolic transfer methods, ho…
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…
CADENT: Gated Hybrid Distillation for Sample-Efficient Transfer in Reinforcement Learning
Mahyar Alinejad, Yue Wang, George Atia
Transfer learning promises to reduce the high sample complexity of deep reinforcement learning (RL), yet existing methods struggle with domain shift between source and target envir…