Reinforcement learning is used in self-driving cars, automated cooling for data centres, recommendation engines, personalized chatbots, trading stocks, etc. In reinforcement learning, there is no right or wrong answer. Instead, the agent that gives the reinforcement decides what to do to finish the task. It differs from supervised learning, where the answer is part of the training data, and the model is taught to find it. It has to learn from its own experience since it doesn't have a training dataset.

Here are the best reinforcement learning resources for 2022:

OpenAI Gym

OpenAI Gym is the most popular platform for making reinforcement learning models and comparing them. It works well with powerful computing libraries like TensorFlow. In addition, the Python-based rich AI simulation environment allows agents to be trained using traditional video games like Atari and other scientific fields like robotics and physics using tools like the Gazebo and MuJoCo simulators.

The gym environment also has APIs for feeding observations to agents and giving them rewards. In addition, OpenAI has made a new platform called Gym Retro, which is now available. It has 58 different scenes from Sonic the Hedgehog, Sonic the Hedgehog 2, and Sonic 3. Fans of reinforcement learning and people who make AI games can sign up for this challenge.

Google's Dopamine

Dopamine works like a cheat code in a video game. People can learn new things from it. Dopamine is a shortcut for doing things in real life. It is made so that researchers who use RL can quickly show their results. It's like Tensorflow, but Google does not make it.

Dopamine tries to be flexible, reliable, and easy to use again and again. The first version focuses on supporting the cutting-edge, single-GPU Rainbow agent, which is used to play Atari 2600 games (Hessel et al., 2018). (Bellemare et al., 2013). To code RL, you need a complicated setup and several steps. You can ease into this with the help of dopamine.

Facebook's ReAgent

Horizon's successor, Reagent, aims to train RL models in a batch environment. Like Facebook, the framework is entirely based on PyTorch. The workflow's first step that the framework helps with is data preparation. The Reagent's objective is real-time deployment rather than quick experimentation.

There are six main algorithms listed in the official literature that you can work on, but with a little creativity, there is room for significant expansion. Utilizing the framework, which focuses on the entire workflow, may produce positive results. The main problem with using this framework is that there isn't a pip installer. You can access the official paper and the source code here.

DeepMind's OpenSpiel

DeepMind is among the most regular contributors to open-source deep learning stacks. Even in 2019, DeepMind at Alphabet, a reinforcement learning framework with a gaming focus, unveiled OpenSpiel. The framework is made up of several settings and computational methods that can be used to support research on general reinforcement learning, mainly when it is applied to gaming. In addition, OpenSpiel provides tools for studying learning dynamics and other famous evaluation metrics and tools for browsing and planning in games.

More than 20 different single- and multi-agent game types, including sequential, cooperative, zero-sum, and one-shot games, are supported by the framework. Additionally, there are games with strict turn requirements, auction games, matrix games, simultaneous-move games, perfect games and games with imperfect information (where decisions are made simultaneously).

Intel AI lab's RL Coach

The Reinforcement Learning Coach (Coach) by Intel AI Lab is a Python reinforcement learning framework with various state-of-the-art algorithms.

It exposes several user-friendly APIs for exploring brand-new RL algorithms. The algorithms, environments, and neural network designs are just a few examples of the library's modular components. As a result, extending and reusing existing components is not too difficult.