blocks|key|847864|text|看看2009年的RL-竞争。问题域之一是俄罗斯方块游戏。前一年也有俄罗斯方块的问题。这是来自第五名决赛的52页最后报告，详细介绍了经纪人是如何工作的。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|847865|entityMap|0|LINK|mutability|MUTABLE|url|http://web.archive.org/web/20090501010345/http://2009.rl-competition.org/domains.php|1|https://code.google.com/p/rl-library/wiki/TetrisJava|2|https://dl.dropboxusercontent.com/s/j9d1t0jffck0tdm/SmartAgent%2520-%2520Creating%2520Reinforcement%2520Learning%2520Tetris%2520AI.pdf^0|8|5|0|K|7|1|1G|7|2|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]|$A|T|B|U|1|V]|$A|W|B|X|1|Y]]|C|$]]|$1|D|3|-4|5|6|7|Z|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]|L|$5|G|H|I|C|$J|M]]|N|$5|G|H|I|C|$J|O]]]]

Take a look at the 2009 <a href="http://web.archive.org/web/20090501010345/http://2009.rl-competition.org/domains.php" rel="noreferrer">RL-competition</a>. One of the problem domains is a <a href="https://code.google.com/p/rl-library/wiki/TetrisJava" rel="noreferrer">tetris game</a>. There was a tetris problem the year before too. Here’s the <a href="https://dl.dropboxusercontent.com/s/j9d1t0jffck0tdm/SmartAgent%20-%20Creating%20Reinforcement%20Learning%20Tetris%20AI.pdf" rel="noreferrer">52-page final report</a> from that year’s fifth-place finalist, which goes into a lot of detail about how the agent worked.

blocks|key|2157919|text|希顿研究电子书非常擅长解释神经网络的概念(用代码)。第四章专门讨论机器学习和网络的各种培训方法。有一个可下载的库和示例应用程序供您查看。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2157920|entityMap|0|LINK|mutability|MUTABLE|url|http://www.heatonresearch.com/book/programming-neural-networks-java-2.html^0|0|4|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

The <a href="http://www.heatonresearch.com/book/programming-neural-networks-java-2.html" rel="nofollow noreferrer">Heaton Research</a> ebook is quite good at explaining neural network concepts (with code). Chapter 4 is dedicated to machine learning and the various training methods for your networks. There is a downloadable library and sample applications for you to look at.

blocks|key|2157999|text|这里有一本关于这一主题的好书：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2158000|机器学习和数据挖掘:原理和算法简介|offset|length|2158001|Igor+Kononenko，Matjaz+Kukar+(2007年6月)|2158002|还请看一下这些开源项目：|2158003|Maja机器学习框架|unordered-list-item|2158004|PIQLE|2158005|埃尔西|2158006|机器人入门手册|2158007|entityMap|0|LINK|mutability|MUTABLE|url|https://rads.stackoverflow.com/amzn/click/com/1904275214|1|http://sourceforge.net/projects/mmlf/|2|http://piqle.wiki.sourceforge.net/PIQLE|3|http://sourceforge.net/projects/elsy/|4|http://roboticsprimer.sourceforge.net/^0|0|0|H|0|0|0|0|0|A|1|0|0|5|2|0|0|3|3|0|0|7|4|0^^$0|@$1|2|3|4|5|6|7|18|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|19|8|@]|9|@$D|1A|E|1B|1|1C]]|A|$]]|$1|F|3|G|5|6|7|1D|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|1E|8|@]|9|@]|A|$]]|$1|J|3|K|5|L|7|1F|8|@]|9|@$D|1G|E|1H|1|1I]]|A|$]]|$1|M|3|N|5|L|7|1J|8|@]|9|@$D|1K|E|1L|1|1M]]|A|$]]|$1|O|3|P|5|L|7|1N|8|@]|9|@$D|1O|E|1P|1|1Q]]|A|$]]|$1|Q|3|R|5|L|7|1R|8|@]|9|@$D|1S|E|1T|1|1U]]|A|$]]|$1|S|3|-4|5|6|7|1V|8|@]|9|@]|A|$]]]|T|$U|$5|V|W|X|A|$Y|Z]]|10|$5|V|W|X|A|$Y|11]]|12|$5|V|W|X|A|$Y|13]]|14|$5|V|W|X|A|$Y|15]]|16|$5|V|W|X|A|$Y|17]]]]

Here is a good book on the subject:

<a href="https://rads.stackoverflow.com/amzn/click/com/1904275214" rel="nofollow noreferrer" rel="nofollow noreferrer">Machine Learning and Data Mining: Introduction to Principles and Algorithms</a> 
by Igor Kononenko, Matjaz Kukar (June, 2007)

Also take a look at these open source projects:

<ul>
<li><a href="http://sourceforge.net/projects/mmlf/" rel="nofollow noreferrer">Maja Machine Learning Framework</a> </li>
<li><a href="http://piqle.wiki.sourceforge.net/PIQLE" rel="nofollow noreferrer">PIQLE</a></li>
<li><a href="http://sourceforge.net/projects/elsy/" rel="nofollow noreferrer">Elsy</a> </li>
<li><a href="http://roboticsprimer.sourceforge.net/" rel="nofollow noreferrer">The Robotics Primer Workbook</a></li>
</ul>

blocks|key|2158117|text|TD-Gammon、gnubackgammon或其他类似项目在游戏中都取得了巨大的成功。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2158118|Sutton+&Barto的书“强化学习:导言”也有一些其他的个案研究。|2158119|entityMap|0|LINK|mutability|MUTABLE|url|http://researchweb.watson.ibm.com/massive/tdl.html|1|http://www.cs.ualberta.ca/%257Esutton/book/ebook/node107.html^0|0|9|0|0|V|4|1|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]]|C|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

<a href="http://researchweb.watson.ibm.com/massive/tdl.html" rel="nofollow noreferrer">TD-Gammon</a>, gnubackgammon, or any other similar project were massive successes in games.

Sutton &amp; Barto's book "Reinforcement Learning: An Introduction" also has some other <a href="http://www.cs.ualberta.ca/%7Esutton/book/ebook/node107.html" rel="nofollow noreferrer">Case Studies</a>.

blocks|key|2158223|text|这个问题确实很古老，但是对于2018年阅读这篇文章的人来说，如果您对现有RL算法的可靠引用感兴趣，我强烈建议您使用OpenAI基线。这些算法是由OpenAI的一组员工实现的，他们对此非常了解，并进行了广泛的微调和调试。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2158224|平心而论，你不需要这些俄罗斯方块，但现在我怀疑家庭作业问题可能涉及一些更复杂的环境。|2158225|https://github.com/openai/baselines|offset|length|2158226|更新：|2158227|在2019年，我还推荐rlpyt：|2158228|https://github.com/astooke/rlpyt|2158229|entityMap|0|LINK|mutability|MUTABLE|url|1^0|0|0|0|Z|0|0|0|0|0|W|1|0^^$0|@$1|2|3|4|5|6|7|V|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|W|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|X|8|@]|9|@$F|Y|G|Z|1|10]]|A|$]]|$1|H|3|I|5|6|7|11|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|12|8|@]|9|@]|A|$]]|$1|L|3|M|5|6|7|13|8|@]|9|@$F|14|G|15|1|16]]|A|$]]|$1|N|3|-4|5|6|7|17|8|@]|9|@]|A|$]]]|O|$P|$5|Q|R|S|A|$T|E]]|U|$5|Q|R|S|A|$T|M]]]]

This question is really old, but for anyone reading this in 2018, I highly recommend you use OpenAI Baselines if you're interested in solid references of existing RL algorithms. These algorithms are implemented by a group of employees at OpenAI who really know this stuff, and have been extensively fine-tuned and debugged.

To be fair, you don't need these for Tetris, but nowadays I suspect homework questions may involve some more sophisticated environments.

<a href="https://github.com/openai/baselines" rel="nofollow noreferrer">https://github.com/openai/baselines</a>

UPDATE:

in 2019, I also recommend rlpyt:

<a href="https://github.com/astooke/rlpyt" rel="nofollow noreferrer">https://github.com/astooke/rlpyt</a>

blocks|key|848207|text|这并不是专门针对强化学习的，但是斯坦福大学有一系列关于Youtube上的机器学习和iTunes的讲座。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|848208|链接到第一节课，大约需要30分钟才能深入了解内容。|848209|entityMap|0|LINK|mutability|MUTABLE|url|http://www.youtube.com/watch?v=UzxYlbK2c7E^0|R|D|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]]|C|$]]|$1|D|3|E|5|6|7|R|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]]]

This is not specific to reinforced learning, but Stanford has a great series of lectures on <a href="http://www.youtube.com/watch?v=UzxYlbK2c7E" rel="nofollow noreferrer">machine learning on Youtube</a> and iTunes.

The link is to the first lecture which takes approximately 30 minutes to dive into the content.

blocks|key|2158195|text|麻布是最近的一个Java库，它提供了许多常见强化学习算法的实现，以及一些环境和有用的工具。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2158196|entityMap|0|LINK|mutability|MUTABLE|url|http://burlap.cs.brown.edu/^0|0|2|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

<a href="http://burlap.cs.brown.edu/" rel="nofollow">Burlap</a> is a recent Java library that provides implementations of many common reinforcement learning algorithms as well as a few environments and useful tools.

blocks|key|848239|text|我建议学习基于java的RL4J。我使用了这个，我很惊讶事情是如何顺利进行的，你甚至可以在一个增强学习算法和Actor批评家算法(称为A3C)中学习LSTM网络。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|848240|下面是链接：https://github.com/deeplearning4j/dl4j-examples/blob/master/rl4j-examples/|offset|length|848241|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/deeplearning4j/dl4j-examples/blob/master/rl4j-examples/^0|0|6|22|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|O|8|@]|9|@$D|P|E|Q|1|R]]|A|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|M]]]]

I would suggest to learn RL4J which is java based.
I was using this and I was amazed how things works smoothly and you can learn even LSTM networks in a reinforcement learning algorithm with Actor Critic algorithm (called A3C)

Here is the link:
<a href="https://github.com/deeplearning4j/dl4j-examples/blob/master/rl4j-examples/" rel="nofollow noreferrer">https://github.com/deeplearning4j/dl4j-examples/blob/master/rl4j-examples/</a>

blocks|key|847945|text|我注意到这个问题已经过时了(10年前)，一个现代RL框架和环境的集合在这里可能很有用。我为此创建了GitHub回购，并打算定期更新它。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|847946|https://github.com/TheMTank/RL-code-resources|offset|length|847947|entityMap|0|LINK|mutability|MUTABLE|url^0|0|0|19|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|N|8|@]|9|@$D|O|E|P|1|Q]]|A|$]]|$1|F|3|-4|5|6|7|R|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|C]]]]

I noticed that this question is quite outdated (10 years old) and that a collection of modern RL frameworks and environments could be useful here. I created GitHub repo for this and intend to update it regularly. 

<a href="https://github.com/TheMTank/RL-code-resources" rel="nofollow noreferrer">https://github.com/TheMTank/RL-code-resources</a>

For an ai-class project I need to implement a reinforcement learning algorithm which beats a simple game of tetris. The game is written in Java and we have the source code. I know the basics of reinforcement learning theory but was wondering if anyone in the SO community had hands on experience with this type of thing.

<ol>
<li>What would your recommended readings be for an implementation of reinforced learning in a tetris game?</li>
<li>Are there any good open source projects that accomplish similar things that would be worth checking out?</li>
</ol>

Edit: The more specific the better, but general resources about the subject are welcomed.

Follow up: 

Thought it would be nice if I posted a followup.

Here's the solution (code and writeup) I ended up with for any future students :).

<a href="http://dl.getdropbox.com/u/30163/AI.PAPER.DESIMONE.GOCHEV.doc" rel="noreferrer">Paper</a> / <a href="http://dl.getdropbox.com/u/30163/tetris_done.tar.gz" rel="noreferrer">Code</a>

Good implementations of reinforcement learning?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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对于一个人工智能项目，我需要实现一个强化学习算法，这比一个简单的俄罗斯方块游戏。游戏是用Java编写的，我们有源代码。我知道强化学习理论的基本知识，但我想知道在SO社区中是否有人对这类事情有经验。在俄罗斯方块游戏中实施强化学习，你推荐的阅读材料是什么？有什么好的开源项目可以完成类似的任务，值得一看吗？编辑:越具体越好，但是关于这个主题的一般资源是受欢迎的。后续行动：我想如果我能贴个跟进的话就太好了

问强化学习的良好实现？
EN

回答 9

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