PyPI 官网下载 | cpprb-10.3.0.tar.gz

    [](https://ymd_h.gitlab.io/cpprb/coverage/) <div class="html" id="org9e465a3"> <p> &lt;img src="<a href="https://pepy.tech/badge/cpprb">https://pepy.tech/badge/cpprb</a>"&gt;&lt;img src="<a href="https://pepy.tech/badge/cpprb/month">https://pepy.tech/badge/cpprb/month</a>"&gt;&lt;img src="<a href="https://pepy.tech/badge/cpprb/week">https://pepy.tech/badge/cpprb/week</a>"&gt; </p> </div>  # Overview cpprb is a python ([CPython](https://github.com/python/cpython/tree/master/Python)) module providing replay buffer classes for reinforcement learning. Major target users are researchers and library developers. You can build your own reinforcement learning algorithms together with your favorite deep learning library (e.g. [TensorFlow](https://www.tensorflow.org/), [PyTorch](https://pytorch.org/)). cpprb forcuses speed, flexibility, and memory efficiency. By utilizing [Cython](https://cython.org/), complicated calculations (e.g. segment tree for prioritized experience replay) are offloaded onto C++. (The name cpprb comes from "C++ Replay Buffer".) In terms of API, initially cpprb referred to [OpenAI Baselines](https://github.com/openai/baselines)' implementation. The current version of cpprb has much more flexibility. Any [NumPy](https://numpy.org/) compatible types of any numbers of values can be stored (as long as memory capacity is sufficient). For example, you can store the next action and the next next observation, too. # Installation cpprb requires following softwares before installation. - C++17 compiler (for installation from source) - [GCC](https://gcc.gnu.org/) (maybe 7.2 and newer) - [Visual Studio](https://visualstudio.microsoft.com/) (2017 Enterprise is fine) - Python 3 - pip Cuurently, [clang](https://clang.llvm.org/), which is a default Xcode C/C++ compiler at Apple macOS, cannot compile cpprb. If you are macOS user, you need to install GCC and set environment values of `CC` and `CXX` to `g++`, or just use virtual environment (e.g. [Docker](https://www.docker.com/)). Step by step installation is described [here](https://ymd_h.gitlab.io/cpprb/installation/install_on_macos/). Additionally, here are user's good feedbacks for installation at [macOS](https://github.com/keiohta/tf2rl/issues/75) and [Ubuntu](https://gitlab.com/ymd_h/cpprb/issues/73). (Thanks!) ## Install from [PyPI](https://pypi.org/) (Recommended) The following command installs cpprb together with other dependencies. pip install cpprb Depending on your environment, you might need `sudo` or `--user` flag for installation. On supported platflorms (Linux x86-64 and Windows amd64), binary packages hosted on PyPI can be used, so that you don't need C++ compiler. On the other platforms, such as macOS, and 32bit or arm-architectured Linux and Windows, you cannot install from binary, and you need to compile by yourself. Please be patient, we plan to support wider platforms in future. If you have any troubles to install from binary, you can fall back to source installation by passing `--no-binary` option to the above pip command. (In order to avoid NumPy source installation, it is better to install NumPy beforehand.) pip install numpy pip install --no-binary cpprb ## Install from source code First, download source code manually or clone the repository; git clone https://gitlab.com/ymd_h/cpprb.git Then you can install in the same way; cd cpprb pip install . For this installation, you need to convert extended Python (.pyx) to C++ (.cpp) during installation, it takes longer time than installation from PyPI. # Usage ## Basic Usage Basic usage is following step; 1. Create replay buffer (`ReplayBuffer.__init__`) 2. Add transitions (`ReplayBuffer.add`) 1. Reset at episode end (`ReplayBuffer.on_episode_end`) 3. Sample transitions (`ReplayBuffer.sample`) ## Example Code Here is a simple example for storing standard environment (aka. `obs`, `act`, `rew`, `next_obs`, and `done`). from cpprb import ReplayBuffer buffer_size = 256 obs_shape = 3 act_dim = 1 rb = ReplayBuffer(buffer_size, env_dict ={"obs": {"shape": obs_shape}, "act": {"shape": act_dim}, "rew": {}, "next_obs": {"shape": obs_shape}, "done": {}}) obs = np.ones(shape=(obs_shape)) act = np.ones(shape=(act_dim)) rew = 0 next_obs = np.ones(shape=(obs_shape)) done = 0 for i in range(500): rb.add(obs=obs,act=act,rew=rew,next_obs=next_obs,done=done) if done: # Together with resetting environment, call ReplayBuffer.on_episode_end() rb.on_episode_end() batch_size = 32 sample = rb.sample(batch_size) # sample is a dictionary whose keys are 'obs', 'act', 'rew', 'next_obs', and 'done' ## Construction Parameters (See also [API reference](https://ymd_h.gitlab.io/cpprb/api/api/cpprb.ReplayBuffer.html)) <table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides"> <colgroup> <col class="org-left" /> <col class="org-left" /> <col class="org-left" /> <col class="org-left" /> </colgroup> <thead> <tr> <th scope="col" class="org-left">Name</th> <th scope="col" class="org-left">Type</th> <th scope="col" class="org-left">Optional</th> <th scope="col" class="org-left">Discription</th> </tr> </thead> <tbody> <tr> <td class="org-left"><code>size</code></td> <td class="org-left"><code>int</code></td> <td class="org-left">No</td> <td class="org-left">Buffer size</td> </tr> <tr> <td class="org-left"><code>env_dict</code></td> <td class="org-left"><code>dict</code></td> <td class="org-left">Yes (but unusable)</td> <td class="org-left">Environment definition (See <a href="https://ymd_h.gitlab.io/cpprb/features/flexible_environment/">here</a>)</td> </tr> <tr> <td class="org-left"><code>next_of</code></td> <td class="org-left"><code>str</code> or array-like of <code>str</code></td> <td class="org-left">Yes</td> <td class="org-left">Memory compression (See <a href="https://ymd_h.gitlab.io/cpprb/features/memory_compression/">here</a>)</td> </tr> <tr> <td class="org-left"><code>stack_compress</code></td> <td class="org-left"><code>str</code> or array-like of <code>str</code></td> <td class="org-left">Yes</td> <td class="org-left">Memory compression (See <a href="https://ymd_h.gitlab.io/cpprb/features/memory_compression/">here</a>)</td> </tr> <tr> <td class="org-left"><code>default_dtype</code></td> <td class="org-left"><code>numpy.dtype</code></td> <td class="org-left">Yes</td> <td class="org-left">Fall back data type</td> </tr> <tr> <td class="org-left"><code>Nstep</code></td> <td class="org-left"><code>dict</code></td> <td class="org-left">Yes</td> <td class="org-left">Nstep configuration (See <a href="https://ymd_h.gitlab.io/cpprb/features/nstep/">here</a>)</td> </tr> <tr> <td class="org-left"><code>mmap_prefix</code></td> <td class="org-left"><code>str</code></td> <td class="org-left">Yes</td> <td class="org-left">mmap file prefix (See <a href="https://ymd_h.gitlab.io/cpprb/features/mmap/">here</a>)</td> </tr> </tbody> </table> ## Notes Flexible environment values are defined by `env_dict` when buffer creation. The detail is described at [document](https://ymd_h.gitlab.io/cpprb/features/flexible_environment/). Since stored values have flexible name, you have to pass to `ReplayBuffer.add` member by keyword. # Features cpprb provides buffer classes for building following algorithms. <table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides"> <colgroup> <col class="org-left" /> <col class="org-left" /> <col class="org-left" /> </colgr