<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[RSS Feed]]></title><description><![CDATA[RSS Feed]]></description><link>http://direct.ecency.com</link><image><url>http://direct.ecency.com/logo512.png</url><title>RSS Feed</title><link>http://direct.ecency.com</link></image><generator>RSS for Node</generator><lastBuildDate>Wed, 15 Apr 2026 23:59:50 GMT</lastBuildDate><atom:link href="http://direct.ecency.com/created/reinforcementlearning/rss.xml" rel="self" type="application/rss+xml"/><item><title><![CDATA[Topics in Artificial Intelligence - Part 7 - Reinforcement Learning - (Technical)]]></title><description><![CDATA[Photo Source Preliminary Definitions Agent: an AI system that interacts with the environment, observes its current state, and takes actions to achieve a specific goal. The agent's goal is to maximize the]]></description><link>http://direct.ecency.com/hive-167922/@kevinnag58/topics-in-artificial-intelligence-part-7-reinforcement-learning-technical</link><guid isPermaLink="true">http://direct.ecency.com/hive-167922/@kevinnag58/topics-in-artificial-intelligence-part-7-reinforcement-learning-technical</guid><category><![CDATA[hive-167922]]></category><dc:creator><![CDATA[kevinnag58]]></dc:creator><pubDate>Sat, 18 Feb 2023 22:45:09 GMT</pubDate><enclosure url="https://images.ecency.com/p/2Qhhdda6QnbewmLm8NhwoBxEay1M6U4AZ7zB9Sh4BYtQvtMkYwZJihXwyvcz9NRyt9DXhspZohFiUYKH9Rhx?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[JP Morgan의  Deep Hedging]]></title><description><![CDATA[1. 두 번 JP Morgan이 발표한 기계학습과 관련한 논문 혹은 보고서를 소개했습니다. JP Morgan이 발행한 기계학습 및 빅데이타 안내서 Morgan Stanley와 JP Morgan이 연구하는 RL 오늘은 JP Morgan의 Hans Bühler이 발표한 Deep Hedging: Hedging Derivatives Under Generic Market]]></description><link>http://direct.ecency.com/ai/@smallake/jpmorgandeephedging-5pr2kof1hw</link><guid isPermaLink="true">http://direct.ecency.com/ai/@smallake/jpmorgandeephedging-5pr2kof1hw</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[smallake]]></dc:creator><pubDate>Tue, 29 Oct 2019 06:48:36 GMT</pubDate><enclosure url="https://images.ecency.com/p/4PYjjVeteBEK2nMxpu52Fe3ENYSf6kB6T8PEucgc2BUwtptnPsswLq4JZKG3Gj7UeVx3T5j4TfqkS6xyQMdbJzKUkaKsRdAakvLtEHWpE8N?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Morgan Stanley와 JP Morgan이 연구하는 RL]]></title><description><![CDATA[1. 제목의 RL이 무엇일까요? 길어서 약어를 사용했지만 아래를 읽으시면 답이 나옵니다.(^^) 2018년 여름 Morgan Stanley가 재미있는 소식을 전하였습니다. Morgan Stanley Hires Ex-SAC Capital Artificial Intelligence Expert 을 보면 펜실바니아 주립대학 교수인 Michael Kearns을 Machine]]></description><link>http://direct.ecency.com/reinforcementlearning/@smallake/morganstanleyjpmorganrl-s0j6s5aisy</link><guid isPermaLink="true">http://direct.ecency.com/reinforcementlearning/@smallake/morganstanleyjpmorganrl-s0j6s5aisy</guid><category><![CDATA[reinforcementlearning]]></category><dc:creator><![CDATA[smallake]]></dc:creator><pubDate>Fri, 01 Feb 2019 01:41:36 GMT</pubDate><enclosure url="https://images.ecency.com/p/5bEGgqB1dEZCRxcAhpqohYmQAd9Kk8Kczk5R3J9iqVsUz5SAkMizggEmu6ainyeZV2hGvF7mGLnEjsEJ2NBRrBRmHWWP9Ljx?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[NN Deep Learning AI Machine - Benchmark 5485]]></title><description><![CDATA[I finally finished my AI Deep Learning NN Machine. Initially I was looking into renting a GPU VPN because of all the massive computing math need to train agents in reinforcement learning ( Q learning ).]]></description><link>http://direct.ecency.com/deeplearning/@gooeypixel/nn-deep-learning-ai-machine-benchmark-5485</link><guid isPermaLink="true">http://direct.ecency.com/deeplearning/@gooeypixel/nn-deep-learning-ai-machine-benchmark-5485</guid><category><![CDATA[deeplearning]]></category><dc:creator><![CDATA[gooeypixel]]></dc:creator><pubDate>Tue, 05 Jun 2018 06:32:51 GMT</pubDate><enclosure url="https://images.ecency.com/p/Y2iXpNqZ2GJKoBWD6DkTJgowzeYQZMFQRD635Aj76GKZx?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[From classic AI techniques to Deep Reinforcement Learning]]></title><description><![CDATA[Building machines that can learn from examples, experience, or even from another machines at human level are the main goal of solving AI. That goal in other words is to create a machine that pass the Turing]]></description><link>http://direct.ecency.com/artifitialintelligence/@fesan81/2jyyxt-from-classic-ai-techniques-to-deep-reinforcement-learning</link><guid isPermaLink="true">http://direct.ecency.com/artifitialintelligence/@fesan81/2jyyxt-from-classic-ai-techniques-to-deep-reinforcement-learning</guid><category><![CDATA[artifitialintelligence]]></category><dc:creator><![CDATA[fesan81]]></dc:creator><pubDate>Tue, 28 Nov 2017 10:29:03 GMT</pubDate><enclosure url="https://images.ecency.com/p/2r8F9rTBenJR3iqPxDrevHK3vDeQGnHc8Wj8C8nehVyJ3m8jKx9f3TnMirgpydfFPhHKX72FXthj43D1QWkevFg5HBQoje9cCuztZN5vYng7kEWbYjQeaM645npobYnEi?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[From classic AI techniques to Deep Reinforcement Learning]]></title><description><![CDATA[Building machines that can learn from examples, experience, or even from another machines at human level are the main goal of solving AI. That goal in other words is to create a machine that pass the Turing]]></description><link>http://direct.ecency.com/artifitialintelligence/@fesan81/2fbu4q-from-classic-ai-techniques-to-deep-reinforcement-learning</link><guid isPermaLink="true">http://direct.ecency.com/artifitialintelligence/@fesan81/2fbu4q-from-classic-ai-techniques-to-deep-reinforcement-learning</guid><category><![CDATA[artifitialintelligence]]></category><dc:creator><![CDATA[fesan81]]></dc:creator><pubDate>Tue, 28 Nov 2017 10:16:09 GMT</pubDate><enclosure url="https://images.ecency.com/p/2r8F9rTBenJR3iqPxDrevHK3vDeQGnHc8Wj8C8nehVyJ3m8jKx9f3TnMirgpydfFPhHKX72FXthj43D1QWkevFg5HBQoje9cCuztZN5vYng7kEWbYjQeaM645npobYnEi?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[From classic AI techniques to Deep Reinforcement Learning]]></title><description><![CDATA[Building machines that can learn from examples, experience, or even from another machines at human level are the main goal of solving AI. That goal in other words is to create a machine that pass the Turing]]></description><link>http://direct.ecency.com/artifitialintelligence/@fesan81/from-classic-ai-techniques-to-deep-reinforcement-learning</link><guid isPermaLink="true">http://direct.ecency.com/artifitialintelligence/@fesan81/from-classic-ai-techniques-to-deep-reinforcement-learning</guid><category><![CDATA[artifitialintelligence]]></category><dc:creator><![CDATA[fesan81]]></dc:creator><pubDate>Tue, 28 Nov 2017 09:50:03 GMT</pubDate><enclosure url="https://images.ecency.com/p/2r8F9rTBenJR3iqPxDrevHK3vDeQGnHc8Wj8C8nehVyJ3m8jKx9f3TnMirgpydfFPhHKX72FXthj43D1QWkevFg5HBQoje9cCuztZN5vYng7kEWbYjQeaM645npobYnEi?format=match&amp;mode=fit" length="0" type="false"/></item></channel></rss>