5 citations · 8 across the 8 of their papers we have counts for
8 papers
Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms
Ezgi Korkmaz
Starting from the utilization of deep neural networks to approximate the state-action value function that led to winning one of the most challenging games, to algorithmic advanceme…
Counteractive RL: Rethinking Core Principles for Efficient and Scalable Deep Reinforcement Learning
Ezgi Korkmaz
Following the pivotal success of learning strategies to win at tasks, solely by interacting with an environment without any supervision, agents have gained the ability to make sequ…
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…
A Survey Analyzing Generalization in Deep Reinforcement Learning
Ezgi Korkmaz
Reinforcement learning research obtained significant success and attention with the utilization of deep neural networks to solve problems in high dimensional state or action spaces…
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…
Investigating Vulnerabilities of Deep Neural Policies
Ezgi Korkmaz
Reinforcement learning policies based on deep neural networks are vulnerable to imperceptible adversarial perturbations to their inputs, in much the same way as neural network imag…