ymd-h / cpprb

Fast Flexible Replay Buffer Library (Mirror repository of https://gitlab.com/ymd_h/cpprb)

cpprb

1 Overview

cpprb is a python (CPython) 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, PyTorch).

cpprb forcuses speed, flexibility, and memory efficiency.

By utilizing Cython, 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’ implementation. The current version of cpprb has much more flexibility. Any NumPy 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.

2 Installation

cpprb requires following softwares before installation.

• C++17 compiler (for installation from source)
  • GCC (maybe 7.2 and newer)
  • Visual Studio (2017 Enterprise is fine)
• Python 3
• pip

Additionally, here are user’s good feedbacks for installation at Ubuntu. (Thanks!)

2.1 Install from PyPI (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, Windows amd64, and macOS x86_64), binary packages hosted on PyPI can be used, so that you don’t need C++ compiler. On the other platforms, such as 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

2.2 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.

3 Usage

3.1 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)

3.2 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'

3.3 Construction Parameters

(See also API reference)

Name	Type	Optional	Discription
size	int	No	Buffer size
env_dict	dict	Yes (but unusable)	Environment definition (See here)
next_of	str or array-like of str	Yes	Memory compression (See here)
stack_compress	str or array-like of str	Yes	Memory compression (See here)
default_dtype	numpy.dtype	Yes	Fall back data type
Nstep	dict	Yes	Nstep configuration (See here)
mmap_prefix	str	Yes	mmap file prefix (See here)

3.4 Notes

Flexible environment values are defined by env_dict when buffer creation. The detail is described at document.

Since stored values have flexible name, you have to pass to ReplayBuffer.add member by keyword.

4 Features

cpprb provides buffer classes for building following algorithms.

Algorithms	cpprb class	Paper
Experience Replay	ReplayBuffer	L. J. Lin
Prioritized Experience Replay	PrioritizedReplayBuffer	T. Schaul et. al.
Multi-step (Nstep) Learning	ReplayBuffer, PrioritizedReplayBuffer
Multiprocess Learning (Ape-X)	MPReplayBuffer MPPrioritizedReplayBuffer	D. Horgan et. al.
Large Batch Experience Replay (LaBER)	LaBERmean, LaBERlazy, LaBERmax	T. Lahire et al.
Reverse Experience Replay (RER)	ReverseReplayBuffer	E. Rotinov
Hindsight Experience Replay (HER)	HindsightReplayBuffer	M. Andrychowicz et al.

cpprb features and its usage are described at following pages:

• Flexible Environment
• Multi-step add
• Prioritized Experience Replay
• Nstep Experience Replay
• Memory Compression
• Map Large Data on File
• Multiprocess Learning (Ape-X)
• Save/Load Transitions

5 Design

5.1 Column-oriented and Flexible

One of the most distinctive design of cpprb is column-oriented flexibly defined transitions. As far as we know, other replay buffer implementations adopt row-oriented flexible transitions (aka. array of transition class) or column-oriented non-flexible transitions.

In deep reinforcement learning, sampled batch is divided into variables (i.e. obs, act, etc.). If the sampled batch is row-oriented, users (or library) need to convert it into column-oriented one. (See doc, too)

5.2 Batch Insertion

cpprb can accept addition of multiple transitions simultaneously. This design is convenient when batch transitions are moved from local buffers to a global buffer. Moreover it is more efficient because of not only removing pure-Python for loop but also suppressing unnecessary priority updates for PER. (See doc, too)

5.3 Minimum Dependency

We try to minimize dependency. Only NumPy is required during its execution. Small dependency is always preferable to avoid dependency hell.

6 Contributing to cpprb

Any contribution are very welcome!

6.1 Making Community Larger

Bigger commumity makes development more active and improve cpprb.

• Star GitLab repository (and/or GitHub Mirror)
• Publish your code using cpprb
• Share this repository to your friend and/or followers.

6.2 Q & A at Forum

When you have any problems or requests, you can check Discussions on GitHub.com. If you still cannot find any information, you can post your own.

We keep issues on GitLab.com and users are still allowed to open issues, however, we mainly use the place as development issue tracker.

6.3 Merge Request (Pull Request)

cpprb follows local rules:

• Branch Name
  • “HotFix_***” for bug fix
  • “Feature_***” for new feature implementation
• docstring
  • Must for external API
  • Numpy Style
• Unit Test
  • Put test code under “test/” directory
  • Can test by python -m unittest <Your Test Code> command
  • Continuous Integration on GitLab CI configured by .gitlab-ci.yaml
• Open an issue and associate it to Merge Request

Step by step instruction for beginners is described at here.

7 Links

7.1 cpprb sites

• Project Site
  • Class Reference
  • Unit Test Coverage
• Main Repository
• GitHub Mirror
• cpprb on PyPI

7.2 cpprb users’ repositories

keiohta/TF2RL

TensorFlow2.x Reinforcement Learning

7.3 Example usage at Kaggle competition

• Ape-X DQN-LAP: SafeGuard & RewardRedesign | Hungry Geese

7.4 Japanese Documents

• 【強化学習】cpprb で Experience Replay を簡単に！| Qiita
• 【強化学習】Ape-X の高速な実装を簡単に！| Qiita
• 【強化学習】自作ライブラリでDQN | Qiita
• 【強化学習】Ape-Xの高速化を実現 | Zenn
• 【強化学習】cpprb に遷移のファイル保存機能を追加 | Zenn

8 License

cpprb is available under MIT license.

9 Citation

We would be very happy if you cite cpprb in your papers.

@misc{Yamada_cpprb_2019,
author = {Yamada, Hiroyuki},
month = {1},
title = {{cpprb}},
url = {https://gitlab.com/ymd_h/cpprb},
year = {2019}
}

• 3rd Party Papers citing cpprb
  • E. Aitygulov and A. I. Panov, “Transfer Learning with Demonstration Forgetting for Robotic Manipulator”, Proc. Comp. Sci. 186 (2021), 374-380, https://doi.org/10.1016/j.procs.2021.04.159
  • T. Kitamura and R. Yonetani, “ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives”, NeurIPS Deep RL Workshop (2021) (arXiv, code)