10 papers · 1 filter
Interactive Distributionally Robust Multi-Agent Learning with General Function Approximation
Debamita Ghosh, George K. Atia, Yue Wang
Model misspecification poses a fundamental challenge in multi-agent reinforcement learning, where transition uncertainty can be amplified by strategic interactions among agents. Di…
Vector Bellman Theory for Multichain Robust Average-Reward Markov Decision Processes
Yue Wang, George Atia
Robust average-reward Markov decision processes provide a fundamental framework for long-term performance optimization under uncertainty, and can have optimal long-run rewards that…
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…
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…
Generative Modeling with Continuous Flows: Sample Complexity of Flow Matching
Mudit Gaur, Prashant Trivedi, Shuchin Aeron +3
Flow matching has recently emerged as a promising alternative to diffusion-based generative models, offering faster sampling and simpler training by learning continuous flows gover…
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…
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…
Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning
Chi Zhang, Ziying Jia, George K. Atia +2
Transfer reinforcement learning aims to derive a near-optimal policy for a target environment with limited data by leveraging abundant data from related source domains. However, it…
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…
Align-Pro: A Principled Approach to Prompt Optimization for LLM Alignment
Prashant Trivedi, Souradip Chakraborty, Avinash Reddy +3
The alignment of large language models (LLMs) with human values is critical as these models become increasingly integrated into various societal and decision-making processes. Trad…