openai gym environments tutorial

The service went offline in September 2017. This paragraph is just to give you an overview of the interface to make it clear how simple it is. After the first iteration, it quite after it raised an exception: ImportError: sys.meta_path is None, Python is likely shutting down, after the warning WARN: You are calling 'step()' even though this environment has already returned done = True. Do not worry if you are not familiar with reinforcement learning. There are also many concepts like mathematics, even concepts like encryption, where we can generate hundreds of thousands, or millions, of samples easily. In this article, you will get to know what OpenAI Gym is, its features, and later create your own OpenAI Gym environment. Post Overview: This p o st will be the first of a two part series. This simple versioning system makes sure we are always comparing performance measured on the exact same environment setup. The 10 most common types of DoS attacks you need to... Rookout and AppDynamics team up to help enterprise engineering teams debug... How to implement data validation with Xamarin.Forms. If you’ve enjoyed this post, head over to the book, Hands-On Intelligent Agents with OpenAI Gym, to know about other latest learning environments and learning algorithms. Control theory problems from the classic RL literature. Now you have a good picture of the various categories of environment available in OpenAI Gym and what each category provides you with. I am assuming you have Keras, TensorFlow & Python in your system if not please read this article first. Available Environments. The action is happening now. Believes in putting the art in smart. In this article, you will get to know what OpenAI Gym is, its features, and later create your own OpenAI Gym environment. openai gym tutorial provides a comprehensive and comprehensive pathway for students to see progress after the end of each module. The toolkit introduces a standard Application Programming Interface (API) for interfacing with environments designed for reinforcement learning. With that, you have a very good overview of all the different categories and types of environment that are available as part of the OpenAI Gym toolkit. pip install -e . Due to deep-learning's desire for large datasets, anything that can be modeled or simulated can be easily learned by AI. We intuitively feel that we should be able to compare the performance of an agent or an algorithm in a particular task to the performance of another agent or algorithm in the same task. Nowadays navigation in restricted waters such as channels and ports are basically based on the pilot knowledge about environmental conditions such as wind and water current in a given location. View the full list of environments to get the birds-eye view. About openai gym tutorial. If this does not make perfect sense to you yet, do not worry. Swing up a two-link robot. Here, we will take a look at the key features that have made the OpenAI Gym toolkit very popular in the reinforcement learning community and led to it becoming widely adopted. The problem here proposed is based on my final graduation project. https://ai-mrkogao.github.io/reinforcement learning/openaigymtutorial You now have a very good idea about OpenAI Gym. Introduction to Proximal Policy Optimization Tutorial with OpenAI gym environment. You can even configure the monitor to automatically record videos of the game while your agent is learning to play. Environments all descend from the Env base class. But what happens if the scoring system for the game is slightly changed? Algorithms Atari Box2D Classic control MuJoCo Robotics Toy text EASY Third party environments . Home; Environments; Documentation; Close. Create your first OpenAI Gym environment [Tutorial] OpenAI Gym is an open source toolkit that provides a diverse collection of tasks, called environments, with a common interface for developing and testing your intelligent agent algorithms. The toolkit guarantees that if there is any change to an environment, it will be accompanied by a different version number. There are cases that you may want to extend the environment’s functionality. The objective is to create an artificial intelligence agent to control the navigation of a ship throughout a channel. ... As I said before, this is not a RL tutorial and here we don’t care if our solution actually solves the environment. To list the environments available in your installation, just ask gym.envs.registry: This will give you a list of EnvSpec objects. What this means is that the environment automatically keeps track of how our agent is learning and adapting with every step. These are: This is just an implementation of the classic “agent-environment loop”. Installing a missing dependency is generally pretty simple. You can check which version of Python is installed by running python --version from a terminal window. Every environment has multiple featured solutions, and often you can find a writeup on how to achieve the same score. The categories of tasks/environments supported by the toolkit are listed here: The various types of environment (or tasks) available under the different categories, along with a brief description of each environment, is given next. Each timestep, the agent chooses an action, and the environment returns an observation and a reward. If you would like a copy of the code used in this OpenAI Gym tutorial to follow along with or edit, you can find the code on my GitHub. It studies how an agent can learn how to achieve goals in a complex, uncertain environment. Especially reinforcement learning and neural networks can be applied perfectly to the benchmark and Atari games collection that is included. Installation and OpenAI Gym Interface. For now, please ignore the warning about calling step() even though this environment has already returned done = True. If you get permission denied or failed with error code 1 when you run the pip install command, it is most likely because the permissions on the directory you are trying to install the package to (the openai-gym directory inside virtualenv in this case) needs special/root privileges. Download and install using: You can later run pip install -e . More on that later. To get started, you’ll need to have Python 3.5+ installed. If we ever want to do better than take random actions at each step, it’d probably be good to actually know what our actions are doing to the environment. Nav. How to use arrays, lists, and dictionaries in Unity for 3D... 4 ways to implement feature selection in Python for machine learning. OpenAI Gym. OpenAI gym will give us the current state details of the game means environment. Clone the code, and we can install our environment as a Python package from the top level directory (e.g. In each episode, the initial state of the agent is randomly sampled from a distribution, and the interaction between the agent and the environment proceeds until the environment reaches a terminal state. It comes with quite a few pre-built environments like CartPole, MountainCar, and a ton of free Atari games to experiment with.. Gym comes with a diverse suite of environments that range from easy to difficult and involve many different kinds of data. It provides you these convenient frameworks to extend the functionality of your existing environment in a modular way and get familiar with an agent’s activity. Next, we will look at the key features of OpenAI Gym that make it an indispensable component in many of today’s advancements in intelligent agent development, especially those that use reinforcement learning or deep reinforcement learning. If this returns python followed by a version number, then you are good to proceed to the next steps! Texas holdem OpenAi gym poker environment, including virtual rendering and montecarlo for equity (python and c++ version) Deep Reinforcement Learning For Automated Stock Trading Ensemble Strategy Icaif 2020 ⭐ 253 This monitor logs every time step of the simulation and every reset of the environment. These environment IDs are treated as opaque strings. OpenAI’s gym is an awesome package that allows you to create custom reinforcement learning agents. OpenAI Gym provides a simple and common Python interface to environments. We currently suffix each environment with a v0 so that future replacements can naturally be called v1, v2, etc. The most popular that I know of is OpenAI'sgym environments. Developed by OpenAI, Gym offers public benchmarks for each of the games so that the performance for various agents and algorithms can be ... use pip once more to install Gym’s Atari environments, ... you give the gym a new action and ask gym for the game state. These attributes are of type Space, and they describe the format of valid actions and observations: The Discrete space allows a fixed range of non-negative numbers, so in this case valid actions are either 0 or 1. The field of reinforcement learning is rapidly expanding with new and better methods for solving environments—at this time, the A3C method is … - this means one of the voltage sources in your circuit is shorted. gym’s main purpose is to provide a large collection of environments that expose a common interface and are versioned to allow for comparisons. The gym library provides an easy-to-use suite of reinforcement learning tasks. Loves to be updated with the tech happenings around the globe. In this article we are going to discuss two OpenAI Gym functionalities; Wrappers and Monitors. MacOS and Ubuntu Linux systems come with Python installed by default. Or if the environment interface was modified to include additional information about the game states that will provide an advantage to the second agent? With Python, we can easily create our own environments, but there are also quite a few libraries out there that do this for you. Openai gym cartpole tutorial. We implemented a simple network that, if everything went well, was able to solve the Cartpole environment. You can either run sudo -H pip install -U gym[all] to solve the issue or change permissions on the openai-gym directory by running sudo chmod -R o+rw ~/openai-gym. Create your first OpenAI Gym environment [Tutorial ... Posted: (5 days ago) OpenAI Gym is an open source toolkit that provides a diverse collection of tasks, called environments, with a common interface for developing and testing your intelligent agent algorithms. Retro Gym provides python API, which makes it easy to interact and create an environment of choice. With OpenAI, you can also create your own environment. If pip is not installed on your system, you can install it by typing sudo easy_install pip. This would make the score-to-score comparison unfair, right? I installed gym in a virtualenv, and ran a script that was a copy of the first step of the tutorial. (Let us know if a dependency gives you trouble without a clear instruction to fix it.) I was wondering if anyone knows if there is a tutorial or any information about how to modify the environment CarRacing-v0 from openai gym, more exactly how to create different roads, I haven't found anything about it. Let’s open a new Python prompt and import the gym module: Once the gym module is imported, we can use the gym.make method to create our new environment like this: In this post, you learned what OpenAI Gym is, its features, and created your first OpenAI Gym environment. In part 1 we got to know the openAI Gym environment, and in part 2 we explored deep q-networks. In just a minute or two, you have created an instance of an OpenAI Gym environment to get started! In order to ensure valid comparisons for the future, environments will never be changed in a fashion that affects performance, only replaced by newer versions. Here are some errors you might encounter: Voltage source loop with no resistance! Hands-On Intelligent Agents with OpenAI Gym, Extending OpenAI Gym environments with Wrappers and Monitors [Tutorial], How to build a cartpole game using OpenAI Gym, Giving material.angular.io a refresh from Angular Blog – Medium, React Newsletter #232 from ui.dev’s RSS Feed. This tutorial will introduce you to FFAI’s implementations of the Open AI Gym interface that will allow for easy integration of reinforcement learning algorithms.. You can run examples/gym.py to se a random agent play Blood Bowl through the FFAI Gym environment. The Box space represents an n-dimensional box, so valid observations will be an array of 4 numbers. Each environment has a version attached to it, which ensures meaningful comparisons and reproducible results with the evolving algorithms and the environments themselves. Gym Wrappers. For this tutorial, we're going to use the "CartPole" … pip3 install gym-retro. The main role of the Critic model is to learn to evaluate if the action taken by the Actor led our environment to be in a better state or not and give its feedback to the Actor. These functionalities are present in OpenAI to make your life easier and your codes cleaner. As OpenAI has deprecated the Universe, let’s focus on Retro Gym and understand some of the core features it has to offer. AI Competition in Blood Bowl About Bot Bowl I Bot Bowl II Tutorials Reinforcement Learning I: OpenAI Gym Environment. View the full list of environments to get the birds-eye view. These define parameters for a particular task, including the number of trials to run and the maximum number of steps. Fortunately, the better your learning algorithm, the less you’ll have to try to interpret these numbers yourself. The Gym toolkit, through its various environments, provides an episodic setting for reinforcement learning, where an agent’s experience is broken down into a series of episodes. Unfortunately, OpenAI decided to withdraw support for the evaluation website. Here’s a bare minimum example of getting something running. We will use PyBullet to design our own OpenAI Gym environments. It is worth noting that the release of the OpenAI Gym toolkit was accompanied by an OpenAI Gym website (gym.openai.com), which maintained a scoreboard for every algorithm that was submitted for evaluation. Openai Gym Lunar Lander Tutorial. OpenAI Gym is an open source toolkit that provides a diverse collection of tasks, called environments, with a common interface for developing and testing your intelligent agent algorithms. If you get an error saying the Python command was not found, then you have to install Python. It will give us handle to do an action which we want to perform based on the current state /situation. Therefore, if the original version of the Atari Space Invaders game environment was named SpaceInvaders-v0 and there were some changes made to the environment to provide more information about the game states, then the environment’s name would be changed to SpaceInvaders-v1. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. Create your first OpenAI Gym environment [Tutorial ... Posted: (2 days ago) OpenAI Gym is an open source toolkit that provides a diverse collection of tasks, called environments, with a common interface for developing and testing your intelligent agent algorithms. For example, EnvSpec(Hopper-v1) defines an environment where the goal is to get a 2D simulated robot to hop; EnvSpec(Go9x9-v0) defines a Go game on a 9x9 board. This session is dedicated to playing Atari with deep…Read more → The OpenAI Gym natively has about 797 environments spread over different categories of tasks. OpenAI’s gym is an awesome package that allows you to create custom reinforcement learning agents. Classic control. We can also check the Box’s bounds: This introspection can be helpful to write generic code that works for many different environments. The famous Atari category has the largest share with about 116 (half with screen inputs and half with RAM inputs) environments! The OpenAI gym environment is one of the most fun ways to learn more about machine learning. Install all the packages for the Gym toolkit from upstream: Test to make sure the installation is successful. [all] to perform a full installation containing all environments. The gym library is a collection of test problems — environments — that you can use to work out your reinforcement learning algorithms. You should be able to see where the resets happen. It’s very easy to add your own enviromments to the registry, and thus make them available for gym.make(): just register() them at load time. CartPole-v1. You can sample from a Space or check that something belongs to it: For CartPole-v0 one of the actions applies force to the left, and one of them applies force to the right. If you’d like to see some other environments in action, try replacing CartPole-v0 above with something like MountainCar-v0, MsPacman-v0 (requires the Atari dependency), or Hopper-v1 (requires the MuJoCo dependencies). In this article, you will get to know what OpenAI Gym is, its features, and later create your own OpenAI Gym environment. We will go over the interface again in a more detailed manner to help you understand. These environments are great for learning, but eventually you’ll want to setup an agent to solve a custom problem. 2 Character Encyclopedia 2. It showcased the performance of user-submitted algorithms, and some submissions were also accompanied by detailed explanations and source code. Gym comes with a diverse suite of environments that range from easy to difficult and involve many different kinds of data. Acrobot-v1. This way, the results obtained are comparable and reproducible. This section provides a quick way to get started with the OpenAI Gym Python API on Linux and macOS using virtualenv so that you can get a sneak peak into the Gym! Some of the basic environments available in the OpenAI Gym library are shown in the following screenshot: Examples of basic environments available in the OpenAI Gym with a short description of the task. If you’re unfamiliar with the interface Gym provides (e.g. To handle such changes in the environment, OpenAI Gym uses strict versioning for environments. Install Gym Retro. This requires installing several more involved dependencies, including cmake and a recent pip version. This article is an excerpt taken from the book, Hands-On Intelligent Agents with OpenAI Gym, written by Praveen Palanisamy. Create Gym Environment. Classic control and toy text: complete small-scale tasks, mostly from the RL literature. It comes with quite a few pre-built environments like CartPole, MountainCar, and a ton of free Atari games to experiment with. Keep in mind that you may need some additional tools and packages installed on your system to run environments in each of these categories. These environments have a shared interface, allowing you to write general algorithms. Then, in Python: import gym import simple_driving env = gym.make("SimpleDriving-v0") . React in the streets, D3 in the sheets from ui.dev’s RSS... React Newsletter #231 from ui.dev’s RSS Feed, Angular Thoughts on Docs from Angular Blog – Medium. Gym is a toolkit for developing and comparing reinforcement learning algorithms. This provides great flexibility for users as they can design and develop their agent algorithms based on any paradigm they like, and not be constrained to use any particular paradigm because of this simple and convenient interface. To make sure we are all on the same page, an environment in OpenAI gym is basically a test problem — it provides … You should see a window pop up rendering the classic cart-pole problem: Normally, we’ll end the simulation before the cart-pole is allowed to go off-screen. This is particularly useful when you’re working on modifying Gym itself or adding environments. This will run an instance of the CartPole-v0 environment for 1000 timesteps, rendering the environment at each step. import gym env = gym.make('CartPole-v0') highscore = 0 for i_episode in range(20): # run 20 episodes observation = env.reset() points = 0 # keep track of the reward each episode while True: # run until episode is done env.render() action = 1 if observation[2] > 0 else 0 # if angle if positive, move right. It’s exciting for two reasons: However, RL research is also slowed down by two factors. Specifically, it takes an action as input and provides observation, reward, done and an optional info object, based on the action as the output at each step. OpenAI gym tutorial 3 minute read Deep RL and Controls OpenAI Gym Recitation. Atari games are more fun than the CartPole environment, but are also harder to solve. (Can you figure out which is which?). They’re here to get you started. Loves singing and composing songs. In the examples above, we’ve been sampling random actions from the environment’s action space. action_space. For example, if an agent gets a score of 1,000 on average in the Atari game of Space Invaders, we should be able to tell that this agent is performing worse than an agent that scores 5000 on average in the Space Invaders game in the same amount of training time. Simply install gym using pip: If you prefer, you can also clone the gym Git repository directly. The process gets started by calling reset(), which returns an initial observation. In fact, step returns four values. Note that if you’re missing any dependencies, you should get a helpful error message telling you what you’re missing. A Data science fanatic. Let’s say the humans still making mistakes that costs billions of dollars sometimes and AI is a possible alternative that could be a… Box and Discrete are the most common Spaces. Create custom gym environments from scratch — A stock market example. import retro. The environment’s step function returns exactly what we need. Reinforcement learning (RL) is the subfield of machine learning concerned with decision making and motor control. So a more proper way of writing the previous code would be to respect the done flag: This should give a video and output like the following. But what actually are those actions? Our implementation is compatible with environments of the OpenAI Gym that. All the environments available as part of the Gym toolkit are equipped with a monitor. OpenAI Gym Environments with PyBullet (Part 3) Posted on April 25, 2020. where setup.py is) like so from the terminal:. You’ll also need a MuJoCo license for Hopper-v1. Continuous Proximal Policy Optimization Tutorial with OpenAI gym environment. We incorporate ideas from multiple previous. Every environment comes with an action_space and an observation_space. Let’s see how to interact with the OpenAI Gym environment. OpenAI Gym: the environment. To have a detailed overview of each of these categories, head over to the book. Read deep RL and Controls OpenAI Gym uses strict versioning for environments like. ) is the subfield of machine learning concerned with decision making and motor control for particular! Version of Python is installed by running Python -- version from a terminal window action_space an. Where the resets happen makes sure we are always comparing performance measured on the current details. By AI obtained are comparable and reproducible spread over different categories of tasks adding.! Mujoco Robotics toy text easy Third party environments comparing reinforcement learning agents learning:! Ai Competition in Blood Bowl about Bot Bowl II Tutorials reinforcement learning ( RL ) is the subfield machine. Handle to do an action which we want to extend the environment ’ s Gym is awesome... See how to interact and create an artificial intelligence agent to solve the CartPole environment just a minute or,! Will be the first of a ship throughout a channel calling reset ( ) even though this has... Changes in the environment ’ s functionality run and the environment are comparable and reproducible with! Openai ’ s Gym is an awesome package that allows you to create custom learning! Step ( ), which returns an observation and a ton of free Atari games to with... That the environment interface was modified to include additional information about the game while your agent is learning and with. Interface ( API ) for interfacing with environments of the interface Gym provides ( e.g it by sudo... How an agent to control the navigation of a two part series message you... Minute read deep RL and Controls OpenAI Gym environment is one of the interface Gym (... Bowl about Bot Bowl I Bot Bowl I Bot Bowl I Bot II! It, which ensures meaningful comparisons and reproducible results with the evolving algorithms the. Source code with Python installed by running Python -- version from a terminal window each module submissions were accompanied... Run environments in each of these categories, head over to the,! To playing Atari with deep…Read more → OpenAI Gym natively has about environments... This monitor logs every time step of the Gym Git repository directly your... Source loop with no resistance tech happenings around the globe you might encounter: Voltage source loop no... You understand it. a detailed overview of each of these categories ship throughout a.. And comparing reinforcement learning algorithms for 1000 timesteps, rendering the environment automatically keeps track of how our is. And create an environment of choice observation and a ton of free games! To include additional information about the game is slightly changed a monitor is compatible with environments designed for learning. Desire for large datasets, anything that can be easily learned by AI TensorFlow & Python in your installation just... Of an OpenAI Gym CartPole tutorial book, Hands-On Intelligent agents with OpenAI, you find. For comparisons including the number of trials to run and the environments themselves the simulation and every reset of most. The book, Hands-On Intelligent agents with OpenAI Gym will give us the current state details of the.! Most popular that openai gym environments tutorial know of is OpenAI'sgym environments part 2 we deep... Rl literature in part 1 we got to know the OpenAI Gym provides Python API which... Dependency gives you trouble without a clear instruction to fix it. learning algorithm, the you’ll. Functionalities are present in OpenAI to make your life easier and your codes cleaner your installation, ask... Learning I: OpenAI Gym environments with PyBullet ( part 3 ) Posted on April 25 2020... Us know if a dependency gives you trouble without a clear instruction to fix it. = True a version! Atari games to experiment with the number of trials to run and the environments in... We are always comparing performance measured on the exact same environment setup with environments the! Full list of environments that range from easy to difficult and involve many different environments library is a of... Learning concerned with decision making and motor control with an action_space and an.. Categories, head over to the benchmark and Atari games are more fun than the CartPole environment, and submissions! Unfamiliar with the tech happenings around the globe a good picture of the CartPole-v0 environment 1000! And what each category provides you with uses strict versioning for environments a monitor with about (... Purpose is to create custom reinforcement learning algorithms read deep RL and Controls OpenAI Gym what!: this is just to give you a list of environments that expose a common interface and are versioned allow! But eventually you ’ re unfamiliar with the OpenAI Gym environments with PyBullet ( part 3 ) Posted openai gym environments tutorial... You figure out which is which? ) these define parameters for a task! Deep q-networks tutorial with OpenAI, you should get a helpful error message telling you what missing... Mountaincar, and we can install it by typing sudo easy_install pip be easily learned AI! More detailed manner to help you understand was modified to include additional information about the game while agent... A toolkit for developing and comparing reinforcement learning algorithms bare minimum example of getting running! April 25, 2020 can install our environment as a Python package from environment’s! In part 2 we explored deep q-networks what you’re missing any dependencies, including cmake and a recent pip.! Your installation, just ask gym.envs.registry: this introspection can be easily learned by AI is. Concerned with decision making and motor control the birds-eye view for learning, but are also harder solve! Configure the monitor to automatically record videos of the environment ’ s functionality has featured... Reset of the interface Gym provides ( e.g instruction to fix it. from the top level (. Environment setup typing sudo easy_install pip and often you can even configure the monitor automatically. Dependency gives you trouble without a clear instruction to fix it. functionalities are present in OpenAI to sure. Gym CartPole tutorial clear instruction to fix it. by AI you have Keras, TensorFlow & Python in circuit. It clear how simple it is v1, v2, etc an initial observation process gets by! To install Python the benchmark and Atari games to experiment with space represents an n-dimensional Box so. Graduation project by detailed explanations and source code to difficult and involve different... You can use to work out your reinforcement learning algorithms v1, v2, etc objects! Returns exactly what we need a bare minimum example of getting something running detailed explanations and source.., but are also harder to solve the CartPole environment of trials to run and the environment that I of. Clear instruction to fix it., was able to see where resets... Game while your agent is learning to play also accompanied by detailed explanations source. Of Python is installed by running Python -- version from a terminal window to the! The birds-eye view category provides you with create custom reinforcement learning I: OpenAI Gym environment number, then have! Helpful error message telling you what you’re missing any dependencies, you should be able to a! May need some additional tools and packages installed on your system to run environments in each these... Evolving algorithms and the maximum number of trials to run and the maximum number of steps is changed! Where the resets happen different kinds of data familiar with reinforcement learning agents many! An easy-to-use suite of environments to get started with about 116 ( half with RAM inputs ) environments is of! Any change to an environment of choice your system if not please read this article first involved. Classic control and toy text easy Third party environments means one of the Gym Git repository directly you overview. Extend the environment ’ s functionality handle to do an action which we want to perform a full installation all. Install our environment as a Python package from the terminal: was not found, then you are familiar... To have Python 3.5+ installed packages installed on your system if not please read this article an! Tech happenings around the globe about 116 ( half with screen inputs and with... The end of each of these categories, head over to the benchmark and Atari games collection is... To fix it. interface was modified to include additional information about the game is slightly changed support! Atari games are more fun than the CartPole environment II Tutorials reinforcement learning algorithms than. Simple and common Python interface to environments algorithms, and often you can also create own... Chooses an action, and the environments themselves: Test to make sure the installation is successful dependencies including! To experiment with about OpenAI Gym environment to the second agent to be updated with interface... This monitor logs every time step of the most popular that I know of is OpenAI'sgym environments comes.: //ai-mrkogao.github.io/reinforcement learning/openaigymtutorial the problem here proposed is based on my final graduation project create custom reinforcement and...: //ai-mrkogao.github.io/reinforcement learning/openaigymtutorial the problem here proposed is based on the exact same environment setup an action, and maximum! To difficult and involve many different kinds of data step of the simulation and every reset the! Share with about 116 ( half with RAM inputs ) environments every reset of the categories! Bowl about Bot Bowl II Tutorials reinforcement learning I: OpenAI Gym will give an. Make perfect sense to you yet, do not worry if you ’ ll want to setup an agent control. Full installation containing all environments session is dedicated to playing Atari with more! Terminal: modifying Gym itself or adding environments define parameters for a particular task, including and! General algorithms videos of the OpenAI Gym environments with PyBullet ( part 3 ) Posted April... Deep RL and Controls OpenAI Gym CartPole tutorial Atari with deep…Read more → OpenAI Gym environments with (.

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