Furong Huang

Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems

2022 · arXiv (Cornell University)

Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions. However, when deploying trained communicative agents in a real-world application where noise and potential attackers exist, …

Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with\n Recurrent Networks

2021 · arXiv (Cornell University)

Deep neural networks are powerful machines for visual pattern recognition,\nbut reasoning tasks that are easy for humans may still be difficult for neural\nmodels. Humans possess the ability to extrapolate reasoning strategies learned\non simple problems to …

COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL

2023 · arXiv (Cornell University)

Dyna-style model-based reinforcement learning contains two phases: model rollouts to generate sample for policy learning and real environment exploration using current policy for dynamics model learning. However, due to the complex real-world environment, it is …

Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning

2023 · arXiv (Cornell University)

Multi-agent reinforcement learning (MARL) plays a pivotal role in tackling real-world challenges. However, the seamless transition of trained policies from simulations to real-world requires it to be robust to various environmental uncertainties. Existing works focus …

Balancing Label Imbalance in Federated Environments Using Only Mixup and Artificially-Labeled Noise

2024 · arXiv (Cornell University)

Clients in a distributed or federated environment will often hold data skewed towards differing subsets of labels. This scenario, referred to as heterogeneous or non-iid federated learning, has been shown to significantly hinder model training …