物联网项目实战开发之基于stm32+esp8266 wifi连接阿里云智能生活飞燕平台,实现app

云计算通过物联网设备连接，实现智能家居和工业自动化 #生活知识# #科技生活# #科技改变生活# #云计算#

STM32+W5500 Modbus-TCP协议功能实现

经过这几天的学习与调试，终于在STM32F103VCT6+W5500(SPI1)+Freemodbus 平台上，实现Modbus-TCP协议的功能。其实很简单，只要熟悉Modbus-RTU通讯，明白Modbus帧的结构等，Modbus-TCP只是在原来的帧结构上加个头，去个尾，然后用TCP传输即可。 关键的内容就是怎样获取W5500新接收的数据包，并发送给Modbus事件状态机驱动协议的执行，数据的处理。 主要参考Freemodbus demo里的Modbus-TCP协议实现的思路，获取缓存区的读写与发送响应。

glibc-static-2.17-55.el7.x86_64.rpm

centos7安装oracle报错，需要这个glibc-static-2.17-55.el7.x86_64.rpm

F1C600手册

全志F1C600芯片的F1C600_Datasheet、F1C600_User_Manual。

deep q_learning

# Deep Reinforcement Learning for Keras [](https://travis-ci.org/matthiasplappert/keras-rl) [](http://keras-rl.readthedocs.io/) [](https://github.com/matthiasplappert/keras-rl/blob/master/LICENSE) [](https://gitter.im/keras-rl/Lobby) ## What is it? `keras-rl` implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library [Keras](http://keras.io). Just like Keras, it works with either [Theano](http://deeplearning.net/software/theano/) or [TensorFlow](https://www.tensorflow.org/), which means that you can train your algorithm efficiently either on CPU or GPU. Furthermore, `keras-rl` works with [OpenAI Gym](https://gym.openai.com/) out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend `keras-rl` according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. In a nutshell: `keras-rl` makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and thus Theano or TensorFlow and was built with OpenAI Gym in mind. ## What is included? As of today, the following algorithms have been implemented: - Deep Q Learning (DQN) [[1]](http://arxiv.org/abs/1312.5602), [[2]](http://home.uchicago.edu/~arij/journalclub/papers/2015_Mnih_et_al.pdf) - Double DQN [[3]](http://arxiv.org/abs/1509.06461) - Deep Deterministic Policy Gradient (DDPG) [[4]](http://arxiv.org/abs/1509.02971) - Continuous DQN (CDQN or NAF) [[6]](http://arxiv.org/abs/1603.00748) - Cross-Entropy Method (CEM) [[7]](http://learning.mpi-sws.org/mlss2016/slides/2016-MLSS-RL.pdf), [[8]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.81.6579&rep=rep1&type=pdf) - Dueling network DQN (Dueling DQN) [[9]](https://arxiv.org/abs/1511.06581) - Deep SARSA [[10]](http://people.inf.elte.hu/lorincz/Files/RL_2006/SuttonBook.pdf) You can find more information on each agent in the [wiki](https://github.com/matthiasplappert/keras-rl/wiki/Agent-Overview). I'm currently working on the following algorithms, which can be found on the `experimental` branch: - Asynchronous Advantage Actor-Critic (A3C) [[5]](http://arxiv.org/abs/1602.01783) Notice that these are **only experimental** and might currently not even run. ## How do I install it and how do I get started? Installing `keras-rl` is easy. Just run the following commands and you should be good to go: ```bash pip install keras-rl ``` This will install `keras-rl` and all necessary dependencies. If you want to run the examples, you'll also have to install `gym` by OpenAI. Please refer to [their installation instructions](https://github.com/openai/gym#installation). It's quite easy and works nicely on Ubuntu and Mac OS X. You'll also need the `h5py` package to load and save model weights, which can be installed using the following command: ```bash pip install h5py ``` Once you have installed everything, you can try out a simple example: ```bash python examples/dqn_cartpole.py ``` This is a very simple example and it should converge relatively quickly, so it's a great way to get started! It also visualizes the game during training, so you can watch it learn. How cool is that? Unfortunately, the documentation of `keras-rl` is currently almost non-existent. However, you can find a couple of more examples that illustrate the usage of both DQN (for tasks with discrete actions) as well as for DDPG (for tasks with continuous actions). While these examples are not replacement for a proper documentation, they should be enough to get started quickly and to see the magic of reinforcement learning yourself. I also encourage you to play around with other environments (OpenAI Gym has plenty) and maybe even try to find better hyperparameters for the existing ones. If you have questions or problems, please file an issue or, even better, fix the problem yourself and submit a pull request! ## Do I have to train the models myself? Training times can be very long depending on the complexity of the environment. [This repo](https://github.com/matthiasplappert/keras-rl-weights) provides some weights that were obtained by running (at least some) of the examples that are included in `keras-rl`. You can load the weights using the `load_weights` method on the respective agents. ## Requirements - Python 2.7 - [Keras](http://keras.io) >= 1.0.7 That's it. However, if you want to run the examples, you'll also need the following dependencies: - [OpenAI Gym](https://github.com/openai/gym) - [h5py](https://pypi.python.org/pypi/h5py) `keras-rl` also works with [TensorFlow](https://www.tensorflow.org/). To find out how to use TensorFlow instead of [Theano](http://deeplearning.net/software/theano/), please refer to the [Keras documentation](http://keras.io/#switching-from-theano-to-tensorflow). ## Documentation We are currently in the process of getting a proper documentation going. [The latest version of the documentation is available online](http://keras-rl.readthedocs.org). All contributions to the documentation are greatly appreciated! ## Support You can ask questions and join the development discussion: - On the [Keras-RL Google group](https://groups.google.com/forum/#!forum/keras-rl-users). - On the [Keras-RL Gitter channel](https://gitter.im/keras-rl/Lobby). You can also post **bug reports and feature requests** (only!) in [Github issues](https://github.com/matthiasplappert/keras-rl/issues). ## Running the Tests To run the tests locally, you'll first have to install the following dependencies: ```bash pip install pytest pytest-xdist pep8 pytest-pep8 pytest-cov python-coveralls ``` You can then run all tests using this command: ```bash py.test tests/. ``` If you want to check if the files conform to the PEP8 style guidelines, run the following command: ```bash py.test --pep8 ``` ## Citing If you use `keras-rl` in your research, you can cite it as follows: ```bibtex @misc{plappert2016kerasrl, author = {Matthias Plappert}, title = {keras-rl}, year = {2016}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/matthiasplappert/keras-rl}}, } ``` ## Acknowledgments The foundation for this library was developed during my work at the [High Performance Humanoid Technologies (H²T)](https://h2t.anthropomatik.kit.edu/) lab at the [Karlsruhe Institute of Technology (KIT)](https://kit.edu). It has since been adapted to become a general-purpose library. ## References 1. *Playing Atari with Deep Reinforcement Learning*, Mnih et al., 2013 2. *Human-level control through deep reinforcement learning*, Mnih et al., 2015 3. *Deep Reinforcement Learning with Double Q-learning*, van Hasselt et al., 2015 4. *Continuous control with deep reinforcement learning*, Lillicrap et al., 2015 5. *Asynchronous Methods for Deep Reinforcement Learning*, Mnih et al., 2016 6. *Continuous Deep Q-Learning with Model-based Acceleration*, Gu et al., 2016 7. *Learning Tetris Using the Noisy Cross-Entropy Method*, Szita et al., 2006 8. *Deep Reinforcement Learning (MLSS lecture notes)*, Schulman, 2016 9. *Dueling Network Architectures for Deep Reinforcement Learning*, Wang et al., 2016 10. *Reinforcement learning: An introduction*, Sutton and Barto, 2011 ## Todos - Documentation: Work on the documentation has begun but not everything is documented in code yet. Additionally, it would be super nice to have guides for each agents that describe the basic ideas behind it. - TRPO, priority-based memory, A3C, async DQN, ...

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