投稿時間:2023-01-22 14:12:27 RSSフィード2023-01-22 14:00 分まとめ(14件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
python	Pythonタグが付けられた新着投稿 - Qiita	最適輸送距離を使って，簡単にスタイル変換みたいなことをしてみた	https://qiita.com/nagarekawaKitazawa/items/74fa60b992c94e899f5e	httpsww	2023-01-22 13:29:33
js	JavaScriptタグが付けられた新着投稿 - Qiita	DeepLのChrome拡張を入れたらテキストエリアを拡大できなくなる件	https://qiita.com/bty__/items/1ecfe0b0695304d7b279	拡張機能	2023-01-22 13:59:27
AWS	AWSタグが付けられた新着投稿 - Qiita	【IoT入門】MQTTについて1から丁寧に解説する	https://qiita.com/vbxy95xwy/items/3ebb31a94fd3f9cc0b7a	oasis	2023-01-22 13:10:17
Docker	dockerタグが付けられた新着投稿 - Qiita	sqlxをインストールするとfailed to run custom build command for `openssl-sys`となる	https://qiita.com/Sicut_study/items/3ff4e59ec75c7ef1e7c5	alpine	2023-01-22 13:29:09
golang	Goタグが付けられた新着投稿 - Qiita	labstack/echoとmiddleware.Loggerとエラーレスポンス	https://qiita.com/sYamaz/items/755c923088dae4716927	githubcomlabstackecho	2023-01-22 13:56:22
golang	Goタグが付けられた新着投稿 - Qiita	PHPerが学ぶGo言語③ 例外処理	https://qiita.com/pig_buhi555/items/8082984cbfd3033b4ff8	catch	2023-01-22 13:35:43
海外TECH	DEV Community	Reinforcement Learning: A Great Introduction	https://dev.to/anurag629/reinforcement-learning-a-great-introduction-3c24	Reinforcement Learning A Great IntroductionReinforcement Learning RL is a type of machine learning that focuses on training agents e g robots software programs to make decisions in an environment by learning from their experiences The goal of RL is to maximize a reward signal which represents the agent s success in achieving its objectives The basic idea behind RL is that an agent interacts with an environment taking actions and receiving rewards or penalties based on those actions The agent s goal is to learn a policy which is a set of rules for deciding which action to take in a given situation in order to maximize the reward over time The process of RL is typically broken down into four main components The agent This is the entity that is learning and making decisions It can be a robot software program or any other type of system that interacts with an environment The environment This is the world or system in which the agent is operating It can be a physical environment such as a robot operating in a factory or a virtual environment such as a computer game The state This is the current situation or condition of the environment It can include information such as the agent s position the current weather or the state of other objects in the environment The action This is the decision that the agent makes in response to the current state of the environment It can be a physical movement such as moving forward or turning or a more abstract decision such as selecting a menu option RL algorithms use a process called trial and error to learn the best policy The agent takes an action receives a reward or penalty and then updates its policy based on that experience Over time the agent learns which actions lead to the most reward and adjusts its policy accordingly Here is an example of how reinforcement learning works Step Define the problem The agent is a robot that needs to navigate through a maze to reach a goal The robot has to learn to navigate the maze efficiently to reach the goal in the shortest time possible Step Define the environment The maze is a grid of cells where some cells are blocked and some are open The robot can move in any direction up down left right and the goal is located at the end of the maze Step Define the agent The robot is the agent that will navigate through the maze The robot has a set of actions it can take move up down left right and it will receive rewards or punishments based on its actions Step Define the rewards The robot will receive a positive reward for reaching the goal and a negative reward for hitting a wall or getting stuck in a loop Step Start the training The robot starts navigating the maze and makes decisions based on the rewards it receives The robot will try different actions and learn which actions lead to higher rewards Step Update the agent As the robot navigates the maze it updates its knowledge of the environment and the best actions to take The robot s decision making process improves over time as it receives more rewards Step Test the agent After training the robot is tested in a new maze to see if it can navigate efficiently and reach the goal in the shortest time possible In this example the robot learns to navigate the maze efficiently through trial and error and by receiving rewards and punishments This process is similar to how humans learn through experience and feedback Reinforcement learning is used in many real world applications such as self driving cars game AI and robotic control systems There are several types of reinforcement learning including Value based learning In value based learning the agent learns the value of different states or actions The agent uses this knowledge to make decisions based on which action will lead to the highest expected value Policy based learning In policy based learning the agent learns a policy which is a set of rules that determine the best action to take in a given state The agent uses this policy to make decisions without considering the value of different states or actions Model based learning In model based learning the agent learns a model of the environment which it can use to predict the outcome of different actions The agent uses this model to make decisions based on the predicted outcome of different actions Hybrid learning In hybrid learning the agent combines the strengths of multiple types of reinforcement learning For example it may use a value based approach to make decisions while also learning a model of the environment to predict the outcome of different actions Q learning Q learning is a popular value based reinforcement learning algorithm that learns a function called the Q function which estimates the expected future rewards of taking different actions in different states SARSA SARSA is another popular value based reinforcement learning algorithm that learns a function called the state action value function which estimates the expected future rewards of taking different actions in different states Actor Critic Actor Critic is a popular hybrid approach that combines value based and policy based learning The agent uses a value based approach to learn about the environment and a policy based approach to learn the best actions to take in different states Deep Reinforcement Learning It combines the power of deep learning with reinforcement learning where deep neural networks are used to approximate the value function or policy GitHub link Complete Data Science Bootcamp Main Post Complete Data Science Bootcamp	2023-01-22 04:13:09
海外TECH	CodeProject Latest Articles	How I built the first PDF report for KlipTok using IronPDF	https://www.codeproject.com/Articles/5352704/How-I-built-the-first-PDF-report-for-KlipTok-using	How I built the first PDF report for KlipTok using IronPDFAn important goal for me in building the KlipTok web application is to be able to deliver reports that can be downloaded and referenced by streamers and their support teams to help them learn how to grow their online presence	2023-01-22 04:48:00
海外ニュース	Japan Times latest articles	Just 16% of 17- to 19-year-olds in Japan say marriage in the cards for them	https://www.japantimes.co.jp/news/2023/01/22/national/teenager-survey-marriage/	Just of to year olds in Japan say marriage in the cards for themThe low figures seen in the December online survey by Nippon Foundation contrast with of the respondents who said they desire to marry	2023-01-22 13:21:05
海外ニュース	Japan Times latest articles	Wheelchair tennis star Shingo Kunieda retires	https://www.japantimes.co.jp/sports/2023/01/22/tennis/kunieda-retirement/	medals	2023-01-22 13:13:08
ニュース	BBC News - Home	Actor Jeremy Renner broke over 30 bones in snow plough accident	https://www.bbc.co.uk/news/entertainment-arts-64363167?at_medium=RSS&at_campaign=KARANGA	accidentthe	2023-01-22 04:08:03
ニュース	BBC News - Home	Australian Open 2023 results: Iga Swiatek loses to Elena Rybakina, Coco Gauff out to Jelena Ostapenko	https://www.bbc.co.uk/sport/tennis/64363099?at_medium=RSS&at_campaign=KARANGA	Australian Open results Iga Swiatek loses to Elena Rybakina Coco Gauff out to Jelena OstapenkoWorld number one Iga Swiatek loses to Wimbledon champion Elena Rybakina in the Australian Open fourth round with Coco Gauff also out	2023-01-22 04:39:35
ニュース	BBC News - Home	NFL divisional round play-offs: Hobbled Mahomes leads Chiefs over Jaguars, Eagles dominate Giants	https://www.bbc.co.uk/sport/american-football/64362953?at_medium=RSS&at_campaign=KARANGA	NFL divisional round play offs Hobbled Mahomes leads Chiefs over Jaguars Eagles dominate GiantsPatrick Mahomes battles ankle injury as the Kansas City Chiefs beat the Jacksonville Jaguars while the Philadelphia Eagles dominate the New York Giants	2023-01-22 04:33:45
ニュース	Newsweek	「この年齢の子にさせる格好じゃない」 米セレブ娘の「肌見せすぎ」ファッションに批判	https://www.newsweekjapan.jp/stories/culture/2023/01/post-100660.php		2023-01-22 13:10:00