agents | use TensorFlow library for Contextual Bandits | Reinforcement Learning library

 by   tensorflow Python Version: 1.4.0 License: Apache-2.0

kandi X-RAY | agents Summary

kandi X-RAY | agents Summary

agents is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Reinforcement Learning, Tensorflow applications. agents has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install agents' or download it from GitHub, PyPI.

TF-Agents makes implementing, deploying, and testing new Bandits and RL algorithms easier. It provides well tested and modular components that can be modified and extended. It enables fast code iteration, with good test integration and benchmarking. To get started, we recommend checking out one of our Colab tutorials. If you need an intro to RL (or a quick recap), start here. Otherwise, check out our DQN tutorial to get an agent up and running in the Cartpole environment. API documentation for the current stable release is on TF-Agents is under active development and interfaces may change at any time. Feedback and comments are welcome.

            kandi-support Support

              agents has a highly active ecosystem.
              It has 2508 star(s) with 686 fork(s). There are 85 watchers for this library.
              It had no major release in the last 12 months.
              There are 168 open issues and 455 have been closed. On average issues are closed in 66 days. There are 12 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of agents is 1.4.0

            kandi-Quality Quality

              agents has no bugs reported.

            kandi-Security Security

              agents has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              agents is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              agents releases are not available. You will need to build from source code and install.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed agents and discovered the below as its top functions. This is intended to give you an instant insight into agents implemented functionality, and help decide if they suit your requirements.
            • Train an evaluation model
            • Create a q network
            • Load a TF - Agent environment
            • Map a function over an iterable
            • Trains an agent
            • Create a TFA agent
            • Push values to TensorFlow
            • Convert trajectory to n - step transition
            • Validate variable rank
            • Load a TFRecord dataset
            • Create a tf record dataset
            • Calculate the outer rank
            • Create feed - forward layer network
            • Run unit tests
            • Ensures that tensors have matching dtypes
            • Reshape inner dimensions
            • Calculate the observation distribution
            • Creates a TensorSpec from input_spec
            • Generate an action
            • Create a trajectory from an episode
            • Create MLP layers
            • Call the function
            • Create a training step
            • Calculate soft_variables_update
            • Sample a tensor
            • Return a tf Dataset
            Get all kandi verified functions for this library.

            agents Key Features

            No Key Features are available at this moment for agents.

            agents Examples and Code Snippets

            Training ML-Agents-Training Configurations-Behavior Configurations
            C#dot img1Lines of Code : 128dot img1License : Non-SPDX (NOASSERTION)
            copy iconCopy
                trainer_type: ppo
                  # Hyperparameters common to PPO and SAC
                  batch_size: 1024
                  buffer_size: 10240
                  learning_rate: 3.0e-4
                  learning_rate_schedule: linear
                  # PPO-specific  
            Training ML-Agents-Training Configurations-Environment Parameters
            C#dot img2Lines of Code : 63dot img2License : Non-SPDX (NOASSERTION)
            copy iconCopy
                # < Same as above >
            # Add this section
              my_environment_parameter: 3.0
            Academy.Instance.EnvironmentParameters.GetWithDefault("my_environment_parameter", 0.0f);
            copy iconCopy
            from mlagents_envs.environment import UnityEnvironment
            from mlagents_envs.envs import UnityToGymWrapper
            from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
            from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
            from baselines.b  
            faust - agents
            Pythondot img4Lines of Code : 25dot img4License : Non-SPDX
            copy iconCopy
            from decimal import Decimal
            import faust
            from faust.types import StreamT
            from import app
            from .models import Account
            class AccountRecord(faust.Record):
                name: str
                score: float
                active: bool
            async def add_accoun  
            Generate random agents .
            javadot img5Lines of Code : 10dot img5License : Permissive (MIT License)
            copy iconCopy
            private void setupAnts() {
                    IntStream.range(0, numberOfAnts)
                        .forEach(i -> {
                            ants.forEach(ant -> {
                                ant.visitCity(-1, random.nextInt(numberOfCities));
            Start the agents .
            javascriptdot img6Lines of Code : 9dot img6License : Permissive (MIT License)
            copy iconCopy
            function start() {
                  canvas = document.getElementById("canvas");
                  ctx = canvas.getContext("2d");
                  w = new World();
                  w.agents = [new Agent()];
            Import specific data from mongo to pandas dataframe
            Pythondot img7Lines of Code : 13dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df = json_normalize(list(
                        "$match": query
                        "$replaceRoot": {
                            "newRoot": "$statement"
            My web crawler gets stuck on the Dell website. Is it fixable?
            Pythondot img8Lines of Code : 9dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from random_user_agent.user_agent import UserAgent
            from random_user_agent.params import SoftwareName
            user_agent_rotator = UserAgent(software_names=[SoftwareName.CHROME.value], limit=100)
            user_agent = user_agent_rotator.get_random_user_age
            Azure Pipelines - proper way to use Poetry
            Pythondot img9Lines of Code : 14dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
              name: VMAS
            - script: |
               echo Write your commands here
               echo Hello world
               python --version
               poetry --version
              displayName: 'Command Line Script'
            how to check service running on other server with python
            Pythondot img10Lines of Code : 13dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
                        ip = your_ip
                        server_user = your_serviceuser
                        server_pass = your_pass
                        command = f"net use \\\\{ip} {server_pass} /USER:{server_user}"
                        command = f"SC \

            Community Discussions


            Astar Pathfinding Project setting z position of AI agent to incorrect values in 2D project
            Asked 2021-Jun-14 at 02:09

            I'm working on a 2D game in Unity and am using the A* Pathfinding Package from Aron Granberg.

            Everything appears to be working fine. AIPaths are being generated and AI Agents are navigating from point to point and avoiding obstacles as expected. It's fine except for one thing.

            The position.z of the AI Agent is incorrect.

            The spawn origin of the AI Agent has a z of 0, and the target point has a z of 0, yet the AI Agent's z fluctuates between -9 and -1 as it traverses the path. The path itself appears to have a z position of 0 at each waypoint.

            I haven't modified the code in the package at all and just followed the documentation when setting it up for 2D.

            Any ideas what could be causing this?

            NOTE: I haven't included a screenshot of it, but the prefab that is being spawned in as the AI Agent has a transform position of (0,0,0).

            The A-star pathfinder object:

            The AI Agent object (note that the Z pos is not 0):

            The spawn point object that sets the spawn for the AI agent:

            The destination target object that the AI Agent is heading to:



            Answered 2021-Jun-14 at 02:09

            In case anyone else runs into this problem.

            The fix was to add a Rigidbody2D to my AI Agent and set the gravity scale to 0.

            Even though my game doesn't use Unity's physics system for movement and the Astar package can move AI agents by transform, for some reason it requires a Rigidbody to keep the Z position at 0.

            I'm still not really sure why this solves the problem because, when I was debugging the third-party Astar code, it always returned nextPosition values with a Z position of 0 yet the actual position that the AI Agent was updated to had varying Z positions...

            If you have more info, leave a comment and I'll add it to the answer.



            store agent SessionsClient of dialogflow api
            Asked 2021-Jun-13 at 16:08

            I'm integrating dialogflow in my chat, but i'm having a problem. I can't figure out how can i store the session with the agent



            Answered 2021-Jun-13 at 16:08

            I solved this just saving the SessionId and passing this same id in future calls instead of generating a new one



            How can I tell a Microsoft-hosted agent in Azure Devops to preserve the workspace between jobs?
            Asked 2021-Jun-11 at 18:16

            I want to break down a large job, running on a Microsoft-hosted agent, into smaller jobs running sequentially, on the same agent. The large job is organized like this:



            Answered 2021-Jun-11 at 18:16

            You can't ever rely on the workspace being the same between jobs, period -- jobs may run on any one of the available agents, which are spread across multiple working folders and possibly even on different physical machines.

            Have your jobs publish artifacts.




            Selecting nested dictionaries and turning them to a DataFrame in Python
            Asked 2021-Jun-10 at 12:55

            Selecting nested dictionaries and turning them to a DataFrame in Python

            From the nested 'biblio' data below, is there a way of sorting this into a data frame with each key as a column? For example, where 'classifications_cpc' is a column header with the codes as the subsequent values?



            Answered 2021-Jun-10 at 12:55

            Do you want a column for each and every key? or only specific ones? For example, the cited_by key has no value in it.

            However, assign the data you provided to a variable names your_data and try this code:



            FailedPreconditionError while using DDPG RL algorithm, in python, with keras, keras-rl2
            Asked 2021-Jun-10 at 07:00

            I am training a DDPG agent on my custom environment that I wrote using openai gym. I am getting error during training the model.

            When I search for a solution on web, I found that some people who faced similar issue were able to resolve it by initializing the variable.



            Answered 2021-Jun-10 at 07:00

            For now I was able to solve this error by replacing the imports from keras with imports from tensorflow.keras, although I don't know why keras itseld doesn't work



            Comparing cell to various other cells and returning the number of found and not found
            Asked 2021-Jun-09 at 16:20

            Ok, I know this is hard that even me struggling to understand and put a formula for this but let me explain.

            -I have a sheet that has some cells full of tags for agents and others for QA.

            -I want to compare the tags the agents have used against the tags of what the QA has used and which will be counted as a mistake.

            -So I want to return the number of the matches between agent and QA as per the screenshot

            Here is the sheet link:

            Your help is very appreciated <3



            Answered 2021-Jun-09 at 16:20


            Is there a method to randomly generate agents created from a database?
            Asked 2021-Jun-09 at 14:41

            Example of packages I am currently generating agents with parameters read from DB at a source node. These agents in the model are packages of different types (so basically the packages have the same list of parameter names like package name, volume, etc. but with differing parameter values). The issue is that I need to generate these packages randomly, but it is currently generated in sequence of how the packages are listed in DB. Is there any way to amend the code in order to achieve what is needed?

            Snippet of current code:



            Answered 2021-Jun-09 at 06:09

            You can read from the database with code, then shuffle the list to randomize it and then generate the agents with their characteristics.



            DropdownButton giving red screen
            Asked 2021-Jun-08 at 15:38

            I have a screen that has 2 dropdownbuttons (agency and agents).

            When the agency is selected in the first dropdownbutton, the second dropdownbutton, agent, is enabled and populated. When the second dropdownbutton is enabled and populated I am getting an error as seen below. I'm not sure what is happening here. I am getting a red screen where the agent dropdownbutton should be. The rest of the page is fine and I can enter data. Can anyone help please? '''



            Answered 2021-Jun-08 at 15:31

            I think this is the problem



            Is there a way to pipe the smoke test output outside the agent?
            Asked 2021-Jun-08 at 07:02

            I have a release pipeline with a QA/Smoke Test stage, that generates XML files containing test results.

            If i run this manually on my machine, obviously i have access to the XML files and i can see the details but on the agent I cannot since we dont have access to those Microsoft hosted agents to view the files.

            Is there a way to pipe the files "out" in the task for viewing? maybe there's a 3rd marketplace task that can achieve that?

            heres the deployment result:



            Answered 2021-Jun-08 at 07:02

            Is there a way to pipe the files "out" in the task for viewing? maybe there's a 3rd marketplace task that can achieve that?

            You can try with following task:



            Azure DevOps Pipelines Agent Pool via Terraformed Scale Set
            Asked 2021-Jun-07 at 19:26

            I was tasked with creating a Linux-based Scale Set for use with Azure DevOps Pipelines in Terraform.

            I have everything set up for the basics; however, when I click on the Agents tab in the Agent pools area of my DevOps Project, I get the message:

            No agents are connected Azure virtual machine scale set agents will appear here when they are created.

            I assume that I need the agent installed using these instructions.

            What I have done so far:

            1. Terraform my Azure Scale Set using azurerm_linux_virtual_machine_scale_set - I am using UbuntuServer 18.04-LTS
            2. Add the CustomScript extension via azurerm_virtual_machine_scale_set_extension
            3. Pass in a custom commandToExecute parameter read from a file in Terraform
            4. In my DevOps project, add a new Agent pool that uses the Scale Set created

            In my custom script, I have the basic download and unpacking of the Linux agent:



            Answered 2021-Jun-07 at 19:26

            So no one else has to go through this pain.

            The custom_data part works peachy keen. This is the script I needed to get it running:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install agents

            TF-Agents publishes nightly and stable builds. For a list of releases read the Releases section. The commands below cover installing TF-Agents stable and nightly from as well as from a GitHub clone.


            We're eager to collaborate with you! See for a guide on how to contribute. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.
            Find more information at:

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          • PyPI

            pip install agents

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            gh repo clone tensorflow/agents

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