1 citations · 2 across the 4 of their papers we have counts for
4 papers
Understanding and Diagnosing Deep Reinforcement Learning
Ezgi Korkmaz
Deep neural policies have recently been installed in a diverse range of settings, from biotechnology to automated financial systems. However, the utilization of deep neural network…
Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions
Ezgi Korkmaz, Jonah Brown-Cohen
Learning in MDPs with highly complex state representations is currently possible due to multiple advancements in reinforcement learning algorithm design. However, this incline in c…
Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness
Ezgi Korkmaz
Learning from raw high dimensional data via interaction with a given environment has been effectively achieved through the utilization of deep neural networks. Yet the observed deg…
Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs
Ezgi Korkmaz
The use of deep neural networks as function approximators has led to striking progress for reinforcement learning algorithms and applications. Yet the knowledge we have on decision…