QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
For more information, see http://ai.googleblog.com/2018/06/scalable-deep-reinforcement-learning.html.
Видео QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation канала Peter Pastor
Видео QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation канала Peter Pastor
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Industrial robotics and deep reinforcement learning
Setting up a Reinforcement Learning Task with a Real-World Robot
Deep Reinforcement Learning for Robotic Manipulation
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network (IROS 2020)
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning
Control of a Quadrotor with Reinforcement Learning
Talk: Project Dexter: Machine learning and automatic decision-making for robotic manipulation
Deep Visual Foresight for Planning Robot Motion
Robotic Arm Using Deep Learning
Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning
Robotic Assembly System for Electrical Wire Harnesses - Clear Automation
Teaching Artificial Intelligence to Run (NIPS 2017)
Q-Learning Explained - A Reinforcement Learning Technique![ReLMoGen [Explained] Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation](https://i.ytimg.com/vi/I5LAuoMtT5s/default.jpg)
ReLMoGen [Explained] Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation
Graph-based Reinforcement Learning meets Mixed Integer Programs for 3D robot assembly discovery
KolRob: Collaborative Screw Assembly - Efficient human-robot-collaboration
Deep Model-Based RL for Real-World Robotic Control
Deep Q-Network & Dueling network architectures for deep reinforcement learning
Building a Custom Environment for Deep Reinforcement Learning with OpenAI Gym and Python