Top 12 Reinforcement Learning Buyer's Guide & Reviews

Exciting developments are underway in the field of Reinforcement Learning, where machines are being taught to make decisions based on trial and error. This cutting-edge technology is helping us create smarter, more efficient systems that can self-improve over time. From robotics to gaming, the applications are limitless. AIMachine LearningRLFuture Tech

Reinforcement Learning

Reinforcement learning is a subfield of artificial intelligence that allows machines to learn by trial and error. It is a crucial technology that is enabling computers to learn and improve from their mistakes. In today’s fast-paced world, it is important to adopt technologies that can help to enhance efficiency and productivity. Reinforcement learning is one such technology that is taking over various industries.

Reinforcement learning combines the advantages of machine learning, deep learning, and neural networks to improve the decision-making abilities of machines. It involves the process of learning by trial and error, where the machine tries out different strategies and learns from the outcomes of each strategy. This way, it can effectively optimize its decision-making ability to perform better over time.

There are various reinforcement learning software options available in the market today. In this post, we will walk you through some critical factors to consider when looking to buy reinforcement learning software. We have also compiled a list of the top 12 reinforcement learning software options along with their reviews to help you make an informed choice.

1. TensorFlow

TensorFlow is one of the most popular and widely used reinforcement learning software options. It is an open-source software library for dataflow and differentiable programming. TensorFlow is convenient to use, and it has an extensive network of users, making it easy to acquire support when necessary.

2. Keras

Keras is a user-friendly reinforcement learning software option suitable for beginners. It is an open-source library for advanced neural networks, and it assists users in building, training, and evaluating deep learning models. Keras is highly efficient and supports customizable building blocks that make it scalable.

3. PyTorch

PyTorch is an open-source machine learning software library that is ideal for researchers and developers. It offers a flexible and easy-to-use interface that supports deep learning and reinforcement learning. PyTorch also allows for quick deployment and provides a seamless transition from research to production.

4. Theano

Theano is another reinforcement learning software option that is efficient and simple to use. It is a fast and reliable software system that assists in optimizing mathematical expressions, particularly matrix-valued ones. Theano is also suitable for symbolic differentiation, a critical feature of reinforcement learning systems.

5. Caffe

Caffe is a deep learning reinforcement software option that is particularly useful for image classification and recognition. It is fast, portable, and configurable, making it a suitable software option for researchers and developers. The Caffe library is also easily downloadable, making it accessible to most users.

6. Chainer

Chainer is another software library used predominantly in the reinforcement learning domain. It offers support for CUDA and CuDNN, making it a desirable option for researchers who require efficient and fast computation. It also includes useful features such as autograd, which makes it simpler to use.

7. OpenAI Gym

Gym is a reinforcement learning platform developed by OpenAI, a research organization dedicated to AGI development. It offers a range of reinforcement learning tools, including environments, toolsets, and algorithms. OpenAI gym supports several reinforcement learning methods, including Q-Learning and Policy Gradient.

8. RLlib

RLlib is a Python reinforcement learning library developed explicitly for large-scale distributed training. It offers support for several reinforcement learning algorithms and distributed computing frameworks such as Spark and Horovod. RLlib is a transparent option for developers and researchers in the reinforcement learning field.

9. TRFL

TRFL is a reinforcement learning library that focuses on deep reinforcement learning. It assists developers in implementing various policies, including dueling, prioritized, and persistent.

10. Stable Baselines

Stable Baselines is another Python reinforcement learning library that is well-suited for beginners. It provides APIs that enable users to train reinforcement learning algorithms with ease. Stable Baselines is also compatible with most deep learning libraries, including TensorFlow, PyTorch, and Keras.

11. Kung Fu

Kung Fu is a reinforcement learning library from Baidu. It is specifically designed for high-performance distributed reinforcement learning. Kung Fu provides various algorithms, including DDPG and A2C, and it supports parallel training, allowing for faster model training.

12. RoboSchool

RoboSchool is a reinforcement learning package that assists users in developing and simulating various robotic tasks. It offers excellent visualization of the environment and allows for the development of robust algorithms. RoboSchool is also highly customizable, making it an ideal option for researchers in the robotics domain.


Reinforcement learning software is continually evolving, and we have compiled a list of some of the most popular options in the market. It is important to evaluate the requirements of your project, your budget, and the level of user support you require before making a purchase. the software option that you select should be efficient, compatible, and customizable to suit your needs. We hope that this buyer’s guide will assist you in making an informed decision.