M Kiran, M Ozyildirim - arXiv preprint arXiv:2201.11182, 2022 - arxiv.org
Reinforcement learning (RL) applications, where an agent can simply learn optimal behaviors by interacting with the environment, are quickly gaining tremendous success in a …
S Khadka, K Tumer - arXiv preprint arXiv:1805.07917, 2018 - researchgate.net
Abstract Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically suffer from three core …
Despite significant progress in challenging problems across various domains, applying state- of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their …
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and …
S Khadka, K Tumer - Advances in Neural Information …, 2018 - proceedings.neurips.cc
Abstract Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically suffer from three core …
In computer vision and natural language processing, innovations in model architecture that lead to increases in model capacity have reliably translated into gains in performance. In …
Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL. However, the theoretical …
Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that …
Deep Reinforcement Learning (DRL) combines the power of Deep Leaning and Reinforcement learning, and has started gaining a lot of attraction in various domains. Also …