先安装tensorflow 1.2版本和python 3.6，

接着安装：

numpy-1.13.1+mkl-cp36-cp36m-win_amd64.whl

的版本，这个是windows下的，如果linux下直接使用pip install numpy就可以了。

再接着安装scipy版本，也是windows 10下64位版本：

scipy-0.19.1-cp36-cp36m-win_amd64.whl

下载这些文件是通过网站：http://www.lfd.uci.edu/~gohlke/pythonlibs/  ，它是提供WINDOWS的版本。

接着下来，就是安装gym模块：

D:\AI\sample\tensorforce>pip install gym

它的网站连接是https://pypi.python.org/pypi/gym/0.2.0

最后，就是安装tesorforce库：

git clone git@github.com:reinforceio/tensorforce.git
cd tensorforce
pip install -e .

这样就可以运行强化学习的例子：

python examples/openai_gym.py CartPole-v0 -a TRPOAgent -c examples/configs/trpo_cartpole.json -n examples/configs/trpo_cartpole_network.json

结果输出如下：

[2017-07-24 11:05:28,928] Making new env: CartPole-v0
2017-07-24 11:05:30.056045: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
2017-07-24 11:05:30.056185: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.
2017-07-24 11:05:30.057611: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
2017-07-24 11:05:30.059002: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-07-24 11:05:30.059684: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-07-24 11:05:30.060401: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-07-24 11:05:30.061129: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-07-24 11:05:30.061744: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-07-24 11:05:30.406967: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:940] Found device 0 with properties:
name: GeForce GTX 1060 6GB
major: 6 minor: 1 memoryClockRate (GHz) 1.7845
pciBusID 0000:01:00.0
Total memory: 6.00GiB
Free memory: 4.99GiB
2017-07-24 11:05:30.407097: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:961] DMA: 0
2017-07-24 11:05:30.408846: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 0:   Y
2017-07-24 11:05:30.409518: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0)
[2017-07-24 11:05:31,317] Starting TRPOAgent for Environment 'OpenAIGym(CartPole-v0)'
[2017-07-24 11:05:34,728] Finished episode 50 after 16 timesteps
[2017-07-24 11:05:34,728] Episode reward: 16.0
[2017-07-24 11:05:34,729] Average of last 500 rewards: 2.6
[2017-07-24 11:05:34,730] Average of last 100 rewards: 13.0
[2017-07-24 11:05:39,224] Finished episode 100 after 81 timesteps
[2017-07-24 11:05:39,224] Episode reward: 81.0
[2017-07-24 11:05:39,226] Average of last 500 rewards: 6.49
[2017-07-24 11:05:39,226] Average of last 100 rewards: 32.45
[2017-07-24 11:05:47,185] Finished episode 150 after 177 timesteps
[2017-07-24 11:05:47,185] Episode reward: 177.0
[2017-07-24 11:05:47,186] Average of last 500 rewards: 13.364
[2017-07-24 11:05:47,187] Average of last 100 rewards: 53.82
[2017-07-24 11:06:00,082] Finished episode 200 after 136 timesteps
[2017-07-24 11:06:00,083] Episode reward: 136.0
[2017-07-24 11:06:00,084] Average of last 500 rewards: 24.414
[2017-07-24 11:06:00,085] Average of last 100 rewards: 89.62
[2017-07-24 11:06:18,207] Finished episode 250 after 144 timesteps
[2017-07-24 11:06:18,208] Episode reward: 144.0
[2017-07-24 11:06:18,209] Average of last 500 rewards: 40.096
[2017-07-24 11:06:18,210] Average of last 100 rewards: 133.66
[2017-07-24 11:06:35,440] Finished episode 300 after 143 timesteps
[2017-07-24 11:06:35,440] Episode reward: 143.0
[2017-07-24 11:06:35,442] Average of last 500 rewards: 55.014
[2017-07-24 11:06:35,443] Average of last 100 rewards: 153.0
[2017-07-24 11:06:52,119] Finished episode 350 after 169 timesteps
[2017-07-24 11:06:52,119] Episode reward: 169.0
[2017-07-24 11:06:52,120] Average of last 500 rewards: 69.586
[2017-07-24 11:06:52,121] Average of last 100 rewards: 147.45
[2017-07-24 11:07:05,441] Finished episode 400 after 73 timesteps
[2017-07-24 11:07:05,441] Episode reward: 73.0
[2017-07-24 11:07:05,441] Average of last 500 rewards: 81.13
[2017-07-24 11:07:05,441] Average of last 100 rewards: 130.58
[2017-07-24 11:07:15,715] Finished episode 450 after 123 timesteps
[2017-07-24 11:07:15,715] Episode reward: 123.0
[2017-07-24 11:07:15,716] Average of last 500 rewards: 89.798
[2017-07-24 11:07:15,716] Average of last 100 rewards: 101.06
[2017-07-24 11:07:23,885] Finished episode 500 after 51 timesteps
[2017-07-24 11:07:23,885] Episode reward: 51.0
[2017-07-24 11:07:23,886] Average of last 500 rewards: 96.714
[2017-07-24 11:07:23,886] Average of last 100 rewards: 77.92
[2017-07-24 11:07:32,642] Finished episode 550 after 62 timesteps
[2017-07-24 11:07:32,642] Episode reward: 62.0
[2017-07-24 11:07:32,643] Average of last 500 rewards: 101.236
[2017-07-24 11:07:32,643] Average of last 100 rewards: 70.19
[2017-07-24 11:07:42,235] Finished episode 600 after 53 timesteps
[2017-07-24 11:07:42,235] Episode reward: 53.0
[2017-07-24 11:07:42,236] Average of last 500 rewards: 104.336
[2017-07-24 11:07:42,237] Average of last 100 rewards: 70.56
[2017-07-24 11:07:52,850] Finished episode 650 after 72 timesteps
[2017-07-24 11:07:52,851] Episode reward: 72.0
[2017-07-24 11:07:52,851] Average of last 500 rewards: 105.88
[2017-07-24 11:07:52,852] Average of last 100 rewards: 77.04
[2017-07-24 11:08:02,817] Finished episode 700 after 80 timesteps
[2017-07-24 11:08:02,817] Episode reward: 80.0
[2017-07-24 11:08:02,818] Average of last 500 rewards: 102.986
[2017-07-24 11:08:02,818] Average of last 100 rewards: 82.87
[2017-07-24 11:08:13,726] Finished episode 750 after 77 timesteps
[2017-07-24 11:08:13,726] Episode reward: 77.0
[2017-07-24 11:08:13,727] Average of last 500 rewards: 96.038
[2017-07-24 11:08:13,727] Average of last 100 rewards: 84.45
[2017-07-24 11:08:25,828] Finished episode 800 after 59 timesteps
[2017-07-24 11:08:25,828] Episode reward: 59.0
[2017-07-24 11:08:25,829] Average of last 500 rewards: 90.658
[2017-07-24 11:08:25,829] Average of last 100 rewards: 91.36
[2017-07-24 11:08:42,711] Finished episode 850 after 177 timesteps
[2017-07-24 11:08:42,711] Episode reward: 177.0
[2017-07-24 11:08:42,712] Average of last 500 rewards: 90.34
[2017-07-24 11:08:42,712] Average of last 100 rewards: 118.96
[2017-07-24 11:09:03,141] Finished episode 900 after 167 timesteps
[2017-07-24 11:09:03,141] Episode reward: 167.0
[2017-07-24 11:09:03,142] Average of last 500 rewards: 95.556
[2017-07-24 11:09:03,142] Average of last 100 rewards: 155.07
[2017-07-24 11:09:27,219] Finished episode 950 after 200 timesteps
[2017-07-24 11:09:27,219] Episode reward: 200.0
[2017-07-24 11:09:27,220] Average of last 500 rewards: 105.26
[2017-07-24 11:09:27,220] Average of last 100 rewards: 175.66
[2017-07-24 11:09:53,139] Finished episode 1000 after 200 timesteps
[2017-07-24 11:09:53,139] Episode reward: 200.0
[2017-07-24 11:09:53,141] Average of last 500 rewards: 118.344
[2017-07-24 11:09:53,141] Average of last 100 rewards: 191.86
[2017-07-24 11:10:19,722] Finished episode 1050 after 200 timesteps
[2017-07-24 11:10:19,722] Episode reward: 200.0
[2017-07-24 11:10:19,723] Average of last 500 rewards: 131.222
[2017-07-24 11:10:19,724] Average of last 100 rewards: 200.0
[2017-07-24 11:10:45,962] Finished episode 1100 after 200 timesteps
[2017-07-24 11:10:45,963] Episode reward: 200.0
[2017-07-24 11:10:45,963] Average of last 500 rewards: 144.198
[2017-07-24 11:10:45,963] Average of last 100 rewards: 199.83

1. RPG游戏从入门到精通


2. WiX安装工具的使用

3. 俄罗斯方块游戏开发
http://edu.csdn.net/course/detail/51104. boost库入门基础
http://edu.csdn.net/course/detail/50295.Arduino入门基础
http://edu.csdn.net/course/detail/49316.Unity5.x游戏基础入门
http://edu.csdn.net/course/detail/48107. TensorFlow API攻略
http://edu.csdn.net/course/detail/44958. TensorFlow入门基本教程
http://edu.csdn.net/course/detail/43699. C++标准模板库从入门到精通 
http://edu.csdn.net/course/detail/332410.跟老菜鸟学C++
http://edu.csdn.net/course/detail/290111. 跟老菜鸟学python
http://edu.csdn.net/course/detail/259212. 在VC2015里学会使用tinyxml库
http://edu.csdn.net/course/detail/259013. 在Windows下SVN的版本管理与实战 
http://edu.csdn.net/course/detail/257914.Visual Studio 2015开发C++程序的基本使用 
http://edu.csdn.net/course/detail/257015.在VC2015里使用protobuf协议
http://edu.csdn.net/course/detail/258216.在VC2015里学会使用MySQL数据库
http://edu.csdn.net/course/detail/2672

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