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cs.LG2026
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…
cs.LG2026
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…