Results 11 to 20 of about 301,921 (265)
Deep Residual Reinforcement Learning
, 2020 We revisit residual algorithms in both model-free and model-based reinforcement learning settings. We propose the bidirectional target network technique to stabilize residual algorithms, yielding a residual version of DDPG that significantly outperforms ...
Boehmer, Wendelin +2 more
core +3 more sources
Deep Reinforcement Learning that Matters
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to ...
Bachman, Philip +5 more
core +2 more sources
Deep learning, reinforcement learning, and world models
Neural Networks, 2022 Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and with neuroscientific findings.
Yutaka Matsuo +2 more
exaly +4 more sources
Learning Mobile Manipulation through Deep Reinforcement Learning [PDF]
Sensors, 2020 Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator.
Cong Wang +7 more
doaj +4 more sources
Scenario-assisted Deep Reinforcement Learning [PDF]
Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development, 2022 Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements posed by human engineers. In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (
Yerushalmi, Raz +5 more
openaire +2 more sources
Explainability in deep reinforcement learning [PDF]
Knowledge-Based Systems, 2021 Article accepted at Knowledge-Based ...
Heuillet, Alexandre +2 more
openaire +3 more sources
Deep reinforcement learning of transition states [PDF]
Physical Chemistry Chemical Physics, 2021 RL‡can automatically locate the transition states of chemical reactions through deep reinforcement learning of feedback from molecular simulations.
Jun Zhang +7 more
openaire +3 more sources
Off-policy Maximum Entropy Deep Reinforcement Learning Algorithm Based on RandomlyWeighted Triple Q -Learning [PDF]
Jisuanji kexue, 2022 Reinforcement learning is an important branch of machine learning.With the development of deep learning,deep reinforcement learning research has gradually developed into the focus of reinforcement learning research.Model-free off-policy deep ...
FAN Jing-yu, LIU Quan
doaj +1 more source
Target‐driven visual navigation in indoor scenes using reinforcement learning and imitation learning
CAAI Transactions on Intelligence Technology, 2022 Here, the challenges of sample efficiency and navigation performance in deep reinforcement learning for visual navigation are focused and a deep imitation reinforcement learning approach is proposed.
Qiang Fang +3 more
doaj +1 more source
A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation
Sensors, 2023 Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems.
Dong Han +3 more
doaj +1 more source