4 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…
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…
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…