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Read How Reinforcement Learning works to know more.
It might sound theoretical but gigantic firms like Google and Uber have tested out this exceptional mechanism and have been highly successful in cutting edge applied robotics fields such as self driving vehicles. Other top giants including Amazon, Facebook and Microsoft have centralized their innovations around deep reinforcement learning across Automotive, Supply Chain, Networking, Finance and Robotics.
With such humongous achievement, reinforcement learning libraries has caught the Artificial Intelligence developer communities’ eye and have gained prime interest for training agents and reinforcing the behavior of the trained agents. In fact, researchers believe in the tremendous potential of reinforcement learning to address unsolved real world challenges like material discovery, space exploration, drug discovery etc and build much smarter artificial intelligence solutions.
In this article, we will have a look at the most promising open source tools and libraries to start building your reinforcement learning projects on.
OpenAI Gym, the most popular environment for developing and comparing reinforcement learning models, is completely compatible with high computational libraries like TensorFlow. The Python based rich AI simulation environment offers support for training agents on classic games like Atari as well as for other branches of science like robotics and physics such as Gazebo simulator and MuJoCo simulator.
The Gym environment also offers APIs which facilitate feeding observations along with rewards back to agents. OpenAI has also recently released a new platform, Gym Retro made up of 58 varied and specific scenarios from Sonic the Hedgehog, Sonic the Hedgehog 2, and Sonic 3 games. Reinforcement learning enthusiasts and AI game developers can register for this competition.
This is an another well-known open-source library by Google followed by more than 95,000 developers everyday in areas of natural language processing, intelligent chatbots, robotics, and more. The TensorFlow community has developed an extended version called TensorLayer providing popular RL modules that can be easily customized and assembled for tackling real-world machine learning challenges.
Keras presents simplicity in implementing neural networks with just a few lines of codes with faster execution. It provides senior developers and principal scientists with a high-level interface to high tensor computation framework, TensorFlow and centralizes on the model architecture. So, if you have any existing RL models written in TensorFlow, just pick the Keras framework and you can transfer the learning to the related machine learning problem.
DeepMind Lab is a Google 3D platform with customization for agent-based AI research. It is utilized to understand how self-sufficient artificial agents learn complicated tasks in large, partially observed environments. With the victory of its AlphaGo program against go players, in early 2016, DeepMind captured the public’s attention. With its three hubs spread across London, Canada and France, the DeepMind team is focussing on core AI fundamentals which includes building a single AI system backed by state-of-the-art methods and distributional reinforcement learning.
To know more about how DeepMind Lab works, read How Google’s DeepMind is creating images with artificial intelligence.
Pytorch, open sourced by Facebook, is another well-known deep learning library adopted by many reinforcement learning researchers. It was recent preferred almost unanimously by top 10 finishers in Kaggle competition. With dynamic neural networks and strong GPU acceleration, Rl practitioners use it extensively to conduct experiments on implementing policy-based agent and to create new adventures. One crazy research project is Playing GridWorld, where Pytorch unchained its capabilities with renowned RL algorithms like policy gradient and simplified Actor-Critic method.
Summing It Up
There you have it, the top tools and libraries for reinforcement learning. The list doesn’t end here, as there is a lot of work happening in developing platforms and libraries for scaling reinforcement learning. Frameworks like RL4J, RLlib are already in development and very soon would be full-fledged available for developers to simulate their models in their preferred coding language.