REINFORCEMENT LEARNING 한국어 뜻 - 한국어 번역

• 강화 학습
• reinforcement learning
• 강화학습

영어에서 Reinforcement learning 을 사용하는 예와 한국어로 번역

Reinforcement learning.
강화 학습.

What should I do next?(reinforcement learning).
무엇 향후 계획? - 강화 학습 (Reinforcement Learning).

Reinforcement learning.
강화 학습이.

Playing Atari with deep reinforcement learning.
Playing atari with deep reinforcement learning 이라는 논문이 있습니다.

Reinforcement learning.
강화 학습은.

Policy gradient is an approach to solve reinforcement learning problems.
정책 그라데이션은 강화 학습 문제를 해결하기위한 접근 방식입니다.

Tagged: reinforcement learning.
태그 보관물: Reinforcement Learning.

Supervised, unsupervised and reinforcement learning;
기계학습의 유형 (Supervised, Unsupervised, Reinforcement Learning).

Reinforcement Learning Evolution Strategies.
강화학습 진화전략 Evolution Strategies.

Playing Atari with deep reinforcement learning.
Playing atari with deep reinforcement learning 이란 논문에서 발표된 것입니다.

Reinforcement learning an introduction.
강화 학습 기초 Reinforcement Learning an introduction.

The reward function R is critically important in reinforcement learning.
Reward function \(R\)은 강화학습에서 매우 중요한 요소이다.

The goal of reinforcement learning is to produce a good policy.
강화 학습의 목표는 좋은 정책을 만드는 것입니다.

Neural Optimizer Search with Reinforcement Learning".
서기호 님의 슬라이드 “Neural Architecture Search with Reinforcement Learning”.

Deep Reinforcement Learning and Control.
고려대학교 박주영 교수 (Deep Reinforcement Learning and Control).

KB Kookmin Bank machine learning and reinforcement learning model development.
KB국민은행 머신러닝 및 강화학습 모델 개발.

Reinforcement learning is often used for robotics, gaming, and navigation.
강화 학습은 로봇, 게임 및 내비게이션에 많이 이용됩니다.

Autonomous driving via the map cloud and reinforcement learning: the AROUND platform.
맵 클라우드와 강화학습을 통한 실내 자율주행, AROUND 플랫폼.

Reinforcement Learning(RL) is a branch of machine learning..
강화 학습(Reinforcement learning)은 기계 학습의 한 영역이다.

Neural architecture search with reinforcement learning.
서기호 님의 슬라이드 “Neural Architecture Search with Reinforcement Learning”.

Review reinforcement learning artificial intelligence.
이주현 교수(한양대) Reinforcement Learning for Artificial Intelligence.

There's a second type of learning called reinforcement learning.
두 번째 단계는 강화 학습(reinforcement learning)이라 불리는 일련의 과정들이다.

Barto, Reinforcement Learning: An Introduction, 2nd edition.
Barto의 Reinforcement Learning: An Introduction second edition을 기반으로 하고 있습니다.

As a result, researchers have studied a number of special cases of reinforcement learning problems.
그 결과로 연구자들은 강화 학습의 특별한 경우들을 연구해왔습니다.

Semi-supervised and reinforcement learning is also used in many cases.
이 밖에도 반지도(semi-supervised) 및 강화 학습(reinforcement learning)이 가끔씩 사용됩니다.

Before we get into the details, let's define a few important notions in reinforcement learning.
세부 사항에 들어가기 전에, 강화 학습의 몇 가지 중요한 개념을 정의해야 합니다.

Reinforcement Learning Model Basic theory, Reinforce DeepQ Network A3C.
강화학습(Reinforcement Learning) 모델 기초이론, Reinforce· DQN(DeepQ Network)· A3C 등.

Development of signal operation optimization algorithm using reinforcement learning(Deep Deterministic Policy Gradient).
강화학습(Deep Deterministic Policy Gradient)을 통한 신호 운영 최적화 알고리즘 개발.

However, in Reinforcement Learning, Evolution Strategies(ES) seem to be making a comeback.
그러나, 강화학습 분야에서는, 진화전략(Evolution Strategies)이 다시 돌아오는 것 같습니다.

This course is part of the Machine Learning and Reinforcement Learning in Finance Specialization.
이 강좌는 Machine Learning and Reinforcement Learning in Finance 전문 분야의 일부입니다.