agents | use TensorFlow library for Contextual Bandits | Reinforcement Learning library
kandi X-RAY | agents Summary
kandi X-RAY | agents Summary
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 tensorflow.org. TF-Agents is under active development and interfaces may change at any time. Feedback and comments are welcome.
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Top functions reviewed by kandi - BETA
- 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
agents Key Features
agents Examples and Code Snippets
Lines of Code : 128License : Non-SPDX (NOASSERTION)
Copybehaviors:
BehaviorPPO:
trainer_type: ppo
hyperparameters:
# Hyperparameters common to PPO and SAC
batch_size: 1024
buffer_size: 10240
learning_rate: 3.0e-4
learning_rate_schedule: linear
# PPO-specific behaviors:
BehaviorY:
# < Same as above >
# Add this section
environment_parameters:
my_environment_parameter: 3.0
Academy.Instance.EnvironmentParameters.GetWithDefault("my_environment_parameter", 0.0f);
behaviors:
BehaviorY:
# 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 from decimal import Decimal
import faust
from faust.types import StreamT
from faustapp.app import app
from .models import Account
class AccountRecord(faust.Record):
name: str
score: float
active: bool
@app.agent()
async def add_accoun private void setupAnts() {
IntStream.range(0, numberOfAnts)
.forEach(i -> {
ants.forEach(ant -> {
ant.clear();
ant.visitCity(-1, random.nextInt(numberOfCities));
function start() {
canvas = document.getElementById("canvas");
ctx = canvas.getContext("2d");
w = new World();
w.agents = [new Agent()];
gofast();
} df = json_normalize(list(
collection.aggregate([
{
"$match": query
},
{
"$replaceRoot": {
"newRoot": "$statement"
}
}
])
)
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_agepool:
name: VMAS
steps:
- script: |
echo Write your commands here
echo Hello world
python --version
poetry --version
displayName: 'Command Line Script'
ip = your_ip
server_user = your_serviceuser
server_pass = your_pass
command = f"net use \\\\{ip} {server_pass} /USER:{server_user}"
os.system(command)
command = f"SC \Community Discussions
Trending Discussions on agents
QUESTION
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:
...ANSWER
Answered 2021-Jun-14 at 02:09In 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.
QUESTION
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
...ANSWER
Answered 2021-Jun-13 at 16:08I solved this just saving the SessionId and passing this same id in future calls instead of generating a new one
QUESTION
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:
...ANSWER
Answered 2021-Jun-11 at 18:16You 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.
i.e.
QUESTION
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?
...ANSWER
Answered 2021-Jun-10 at 12:55Do 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:
QUESTION
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.
...ANSWER
Answered 2021-Jun-10 at 07:00For 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
QUESTION
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: https://docs.google.com/spreadsheets/d/1CijQocy96sWpFID2i1BJBkTnTieOyLjP25Ve7KzAc_E/edit#gid=0
Your help is very appreciated <3
...ANSWER
Answered 2021-Jun-09 at 16:20In L2 I entered
QUESTION
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:
...ANSWER
Answered 2021-Jun-09 at 06:09You can read from the database with code, then shuffle the list to randomize it and then generate the agents with their characteristics.
QUESTION
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? '''
...ANSWER
Answered 2021-Jun-08 at 15:31I think this is the problem
QUESTION
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:
...ANSWER
Answered 2021-Jun-08 at 07:02Is 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:
QUESTION
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:
- Terraform my Azure Scale Set using azurerm_linux_virtual_machine_scale_set - I am using UbuntuServer 18.04-LTS
- Add the CustomScript extension via azurerm_virtual_machine_scale_set_extension
- Pass in a custom
commandToExecuteparameter read from a file in Terraform - 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:
...ANSWER
Answered 2021-Jun-07 at 19:26So 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:
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