README.md

ReplayBuffer

The simple buffer for experience replay. Built for uses in reinforcement learning, computer vision, and other applications where temporal information matters.

Installation

Using pip

Clone this repository:

git clone [email protected]:mattbev/replaybuffer.git

Install using pip:

pip install -e <path_to_repo_base_directory>

From source

This package only uses numpy and python built-ins -- just clone the repo and pip install numpy to use it in your project.

Usage

Documentation is here.

This package is meant to be used for experience replay with any data types. To do that, first initialize the buffer:

from replaybuffer import ReplayBuffer

buffer = ReplayBuffer(max_size=100)
buffer.initialize_buffer("observations")
buffer.initialize_buffer("actions")
buffer.initialize_buffer("rewards")
...

Then, within your project, you can store data:

...
 env = <some environment, e.g., OpenAI Gym>
 obs, reward = env.step(action)
 
 buffer.store(
     observations=obs, # image
     actions=action, # vector
     rewards=reward # float
 )
 # or 
 # buffer.store(
 #    **{
 #        "observations": obs, # image
 #        "actions": action, # vector
 #        "rewards": reward # float
 #    }
 #)
 ...

And then retreive it:

...
replay = buffer.sample(n=10)
<use the replay to revisit past observations, for example>
...

A simple example can be found at samples/basics.py.