Deep Reinforcement Learning

التعلم العميق بالتعزيز

ميدان يجمع بين التعلم العميق (لتمثيل الحالات والسياسات عبر شبكات عصبية) والتعلم بالتعزيز (للتعلم من التجربة والمكافآت)، مما يُتيح للوكلاء التعامل مع فضاءات حالات ضخمة كالبكسلات الخام.

A field combining deep learning (for representing states and policies via neural networks) with reinforcement learning (for learning from experience and rewards), enabling agents to handle enormous state spaces such as raw pixels.

Also translated asالتعلم بالتعزيز العميق

First appears in this corpus in: Learning to Predict by the Methods of Temporal Differences (1988)

Appears in these papers

Asynchronous Methods for Deep Reinforcement Learning2016 in the sky ✦
Asynchronous Methods for Deep Reinforcement Learning2016 in the sky ✦
Discovering Faster Matrix Multiplication Algorithms with Reinforcement Learning2022 in the sky ✦
Continuous Control with Deep Reinforcement Learning2015 in the sky ✦
Continuous Control with Deep Reinforcement Learning2015 in the sky ✦
Human-Level Control Through Deep Reinforcement Learning2015 in the sky ✦
First Return, Then Explore2021 in the sky ✦
Learning Dexterous In-Hand Manipulation2019 in the sky ✦
Learning Dexterous In-Hand Manipulation2019 in the sky ✦
Policy Gradient Methods for Reinforcement Learning with Function Approximation1999 in the sky ✦
Q-Learning1992 in the sky ✦
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation2018 in the sky ✦
Learning to Predict by the Methods of Temporal Differences1988 in the sky ✦