Coz Python can take you High in 2021 – Python Libraries for Reinforcement Learning

The emergence and development of Deep Reinforcement Learning (RL) have increased the demand for their evaluation.

Therefore several important libraries have been developed to test and implement the models reliably and quickly. 

Python is an ocean of libraries and opportunities that makes it a popular programming language.

Here’s unveiling the top libraries of Python for Reinforcement Learning. 

Python Libraries for Reinforcement Learning

1. TensorForce

TensorForce is built on the top of Google’s TensorFlow framework, which is straightforward in it’s usage. 

It is an open-source deep reinforcement learning framework and is compatible with Python 3.

TensorForce Installation

pip3 install tensorforce

from tensorforce.agents import Agent

2. Pyqlearning

Pyqlearning is a library of Python which is used to implement Deep learning and Reinforcement learning. 

Specifically for Deep Q-Network, Multi-agent Deep-Q Network, and Q Learning which can be optimized by Annealing models.

For instance, Adaptive Simulated Annealing, Simulated Annealing, Quantum Monte Carlo method.  

Pyqlearning Installation

pip install pyqlearning 

3. KerasRL

KerasRL effortlessly and effectively implements deep reinforcement learning algorithms with the deep learning library Keras. 

It’s actually easy to work with different algorithms as Keras-RL works with OpenAI Gym wonderfully.

KerasRL Installation

pip install Keras-RL

4. ChainerRL

ChainerRL is an open-source deep enforcement library built on the top of the chainer. 

It enables the implementation of the various state-of-the-art deep reinforcement algorithms in Python using a flexible deep learning framework, i.e., Chainer.

Installation of ChainerRL

pip install chainerrl

5. TF Agents

TF Agents is one of the popular Python libraries which has a modular structure.

It is designed to make the task of implementing, deploying, and testing reinforcement learning algorithms easier. 

Well-tested components are provided by TF Agents that can be easily modified and extended.

Currently, it is under active development, but the present set of components makes it the most promising RL library.

TF Agents Installation

pip install tf-agents

6. MAME RL

MAME RL library of Python allows the user to train your reinforcement learning algorithm on almost any arcade game. 

The toolkit allows the algorithm to step through gameplay while also receiving the internal memory address values and frame data.

This is for the purpose of tracking the game state, and alongside sending the actions to interact with the game. 

Mame RL Installation

pip install MAMEToolkit

7. Stable Baselines

Stable Baselines is a visualization tool that has an excellent documentation and a unified structure for all the algorithms.

The OpenAI Baselines library was not good enough and thus the Stable baselines library was created. 

Stable baselines is a collection of improved implementation of RL algorithms which are based on OpenAI baselines.

Stable Baselines Installation

pip install story-baselines

8. Mushrookm RL

To serve the purpose of tensor computations (examples- TensorFlow, PyTorch) and RL benchmarks (examples- Pybullet, openAI Gym, Deepmind Control Suite) Mushroom RL is a library of Python for Reinforcement Library that provides various Python libraries. 

It only enables the performance and execution of RL experiments providing Deep RL and classical RL algorithms. 

The library adapts to heterogenous learning tasks allowing users to focus on developing an interaction between agent and environment. 

Such feature is possible through on-policy and off-policy learning, shallow and deep RL, batch and online algorithms, and episodic and infinite horizon tasks.

MushroomRL allows the users to make a comparison between different deep reinforcement learning techniques in a simple manner. 

Mushroon RL Installation

pip3 install mushroom_rl 

9. RL_Coach

Reinforcement Learning Coach allows easy experimentation with reinforcement learning algorithms. 

One can use it directly from Python where presets( a Python module that instantiates a graph manager object)  are used to define experiments. 

For running the experiment the graph manager holds the agents and the environments along with some other parameters. It acts as a scheduler that orchestrates the experiment.

RL_Coach Installation

pip3 install rl_coach

# importing rl_coach submodules

from rl_coach.coach import CoachInterface

Summary

So, the above listed are some of the significant libraries of Python for Deep reinforcement learning. 

As ample amounts of deep reinforcement learning libraries have been there, it’s important for you to choose the one that suits best according to your task and also by considering various other criteria.