Reinforcement Learning has emerged as one of the most promising areas of machine learning. Reinforcement Learning techniques — the ability to learn and make decisions through the interaction with the environments has made several breakthroughs in the last few years. From robotics to recommendations, researchers are doing their level best in this domain. In this article, we list down 8 toolkits which can be used to deploy reinforcement learning models.
(The list is in alphabetical order)
1| ChainerRL, a Deep Reinforcement Learning library
ChainerRL is a deep reinforcement learning library implements that has several deep reinforcement algorithms in Python. This toolkit uses a subset of the interface and can be applied to a wide range of problems. It also provides various agents, each of which implements a deep learning reinforcement algorithm.
2| Facebook’s ReAgent
Recently, researchers at Facebook AI Research (FAIR) introduced a reinforcement learning toolkit, ReAgent which is designed to streamline the process of building models which make and rely on decisions. This toolkit not only helps in creating AI-based reasoning systems but is also the first to include policy evaluation that incorporates offline feedback to improve models. The online social media giant has been leveraging this toolkit to drive tens of billions of decisions every day. There are three main resources behind this toolkit, they are models, an evaluator, and a serving platform.
Keras is a popular neural network library which is built in Python. keras-rl is a library for Deep Reinforcement Learning with Keras which implements some state-of-the-art deep reinforcement learning algorithms in Python and seamlessly integrates with Deep Learning library Keras.
PyTorch is one of the most popular Deep Learning libraries written in Python. The library has been applied in various deep reinforcement learning algorithms like Deep Q Learning (DQN), Deep Deterministic Policy Gradients (DDPG), Diversity Is All You Need (DIAYN), Stochastic NNs for Hierarchical Reinforcement Learning (SNN-HRL) and Proximal Policy Optimisation (PPO), among others.
5| RLCard: A Toolkit for Reinforcement Learning
RLCard is an open-source toolkit for reinforcement learning research in card games. It supports various card environments with easy-to-use interfaces, including Blackjack, Leduc Hold’em, Texas Hold’em, and many more. The main goal of this toolkit is to bridge the gap between reinforcement learning and imperfect information games.
6| SURREAL: Open-Source Reinforcement Learning Framework
SURREAL (Scalable Robotic REinforcementlearning ALgorithms) is an open-source framework which facilitates reproducible deep reinforcement learning (RL) research for robot manipulation. The toolkit decomposes a distributed RL algorithm into four components — generation of experience (actors), storage of experience (buffer), updating parameters from experience (learner), and storage of parameters (parameter server). This decoupling of data helps in eliminating the need for global synchronisation and improves scalability.
7| The MAME RL Algorithm Training Toolkit
The MAME RL algorithm training toolkit is a Python library which is used to train the reinforcement learning algorithm on almost any arcade game. This toolkit is currently available on Linux systems and works as a wrapper around MAME. It allows the algorithm to step through gameplay while receiving the frame data and internal memory address values for tracking the games state, along with sending actions to interact with the game.
This popular, open-source machine learning platform includes TF-Agents which is a library for Reinforcement Learning in TensorFlow. In this toolkit, the core element of RL algorithms is implemented as Agents wherein an Agent includes two main responsibilities — defining a policy to interact with the environment and the other is to learn or train that policy from the collected experience.
From beating top chess players to driving better insights for healthcare sector, Reinforcement Learning has been used by researchers to solve a number of real-life problems. The developer community has been utilising these tools for training agents as well as introducing advancements in trained agents. There are also popular platforms like OpenAI Gym, DeepMind Lab, DeepMind Control Suite, etc. where one can develop and compare RL algorithms.
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