imitation | Clean PyTorch implementations of imitation and reward | Reinforcement Learning library
kandi X-RAY | imitation Summary
kandi X-RAY | imitation Summary
This project aims to provide clean implementations of imitation and reward learning algorithms. Currently, we have implementations of Behavioral Cloning, DAgger (with synthetic examples), density-based reward modeling, Maximum Causal Entropy Inverse Reinforcement Learning, Adversarial Inverse Reinforcement Learning, Generative Adversarial Imitation Learning and Deep RL from Human Preferences.
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Top functions reviewed by kandi - BETA
- Train the preference model
- Save trajectories to disk
- Save the policy model to the given path
- Save checkpoint
- Train RLR
- Load an attribute
- Return the value associated with the key
- Load reward function
- Runs analysis
- Forward computation
- Create a venv
- Generate a generator that returns a generator function that returns a generator function
- Generate Transitions with the given policy
- Returns all subdirectories in root_dir
- Forward the forward function
- Generate trajectories for a given policy
- Train the model
- Computes the demonstration state of the experiment
- Execute a runner
- Create a VecEnvEnv
- Clean the notebook
- Creates a LearningAlgorithm instance
- Validates a reward network structure
- Evaluate a policy
- Gather all TensorBoard directories contained in the TensorBoard
- Create a reward network
imitation Key Features
imitation Examples and Code Snippets
Lines of Code : 8License : Strong Copyleft (CC BY-SA 4.0)
Copyfrom imitation.algorithms import bc
bc_trainer = bc.BC(
observation_space=env.observation_space,
action_space=env.action_space,
demonstrations=transitions,
)
from unittest.mock import *
import json
data = mock_open(read_data=json.dumps([
{'id': 1, 'firstName': "User", 'lastName': "Random"},
{'id': 2, 'firstName': "User 2", 'lastName': "Random 2"},
{'id': 3, class CreateNodeBoard(QtWidgets.QWidget):
def __init__(self, parent = None):
# ...
self.targetWidget = None
def mousePressEvent(self, event):
if event.button() == QtCore.Qt.MiddleButton:
widget def fill_by_mean(df):
df["Price"] = df["Price"].fillna(df["Price"].mean())
return df
main_data = main_data.groupby(["Animal_Type", "Cost_Type"]).apply(fill_by_mean)
Pet_ID Animal_Type Cost_Type Price
df['Genre'] = df.Genre.str.split('; ')
df.explode('Genre').groupby('Genre')['Movie'].apply(list)
action [The Avengers]
adventure [The Avengers]
biography for func in [func for func in module.__dict__ if not func.startswith("_")]:
vars()[func] = module.__dict__[func]
MERGED_COLUMN = pd.concat([pd.Series(Movie_recommendation_data.Biography),
pd.Series(Movie_recommendation_data.Drama),
pd.Series(Movie_recommendation_data.Thriller),
pd.Series(Movie_recommendation_data.Comedy),
pd.Series(Movie_recommeclass Env:
# ...lots of other class stuff
def render(self):
if self.window is None:
self.window = pyglet.window.Window(width, height)
@self.window.event
def on_close():
sosascript -e 'Tell application "System Events" to display dialog "Some Funky Message" with title "Hello Matey"'
def get_val(self):
return x
def get_val(self):
return True if x else False
def get_val(self):
if x:
return True
else:
return False
dCommunity Discussions
Trending Discussions on imitation
QUESTION
I am currently training a PPO model for a simulation. The PPO model fails to understand that certain conditions will lead to no reward.
These conditions that lead to no reward are very simple rules. I was trying to use these rules to create an 'expert' that the PPO model could use for imitation learning.
Example of Expert-Based Rules:
If resource A is unavailable, then don't select that resource.
If "X" & "Y" don't match, then don't select those.
Example with Imitations Library
I was looking at the "imitations" python library. The example there shows an expert that is a PPO model with more iterations.
https://github.com/HumanCompatibleAI/imitation/blob/master/examples/1_train_bc.ipynb
Questions:
Is there a way to convert the simple "rule-based" expert into a PPO model which can be used for imitation learning?
Or is there a different approach to using a "rule-based" expert in imitation learning?
...ANSWER
Answered 2022-Apr-09 at 12:30Looking at how behavioural cloning is implemented:
QUESTION
I'm currently working on porting an Android application to iOS. One crucial part of the user-interface is an Android Snackbar; a small box at the bottom of the screen alerting the user of something, while not being a full-fledged dialog. SnackBar image.
I tried using different methods of the built-in UIAlertView from this post: How to implement a pop-up dialog box in iOS? but there was nothing like what I'm inquiring.
I would like to know also if this is even possible with Swift, and if I will have to redesign around this problem. As well as if there are any Github repos or the like to provide a framework.
I have done some research, and have not found any posts that answer my problem. The post at How to implement a pop-up dialog box in iOS? is not really at all what I'm looking for, even though I do realize now that there are frameworks out there to work around this issue, and realize that this is indeed possible. My only question now is what are the best frameworks to use for Snackbar imitation.
My question has been answered.
Thanks!
...ANSWER
Answered 2022-Mar-20 at 18:02SnackBar (along with Toast, PopupDialog, etc.) is a concept baked into Android, and there's no equivalent on iOS.
You can:
- create a custom component, and handle fly-in and fly-out animations, or,
- use external libraries. My go-to is ahmedAlmasri's SnackBar.swift, which resembles a lot of that on Android.
QUESTION
Hi I have a case where I need to call the same method in multiple Tasks. I want to have a possibility to call this method one by one (sync) not in parallel mode. It looks like that:
...ANSWER
Answered 2022-Feb-09 at 15:55You said:
I want [the second attempt] to wait for the first refresh API finish
You can save a reference to your Task and, if found, await it. If not found, then start the task:
QUESTION
I want to make a simple imitation of reading the file using mock_open and then to make a simple test that would show me, the mock_open didn't break anything.
The problem is that the mock_file is like this:
ANSWER
Answered 2022-Jan-17 at 15:20mock_open() mocks the open() builtin function.
Just as you wouldn't do json.loads(open), you can't do json.loads(mock_open()).
On top of that, json.loads() receives a string and not a file. For that use json.load().
Fixed code:
QUESTION
I'm trying to match a certain field and update its data from a file delimited with multiple characters. I'm using this to create an imitation of SQL's UPDATE. This is part of a bigger project to create a mini DBMS with bash.
What I tried:
...ANSWER
Answered 2022-Jan-06 at 17:47Better to use awk here:
QUESTION
Please, read the newest EDIT of this question.
Issue: I need to write a correct benchmark to compare a different work using different Thread Pool realizations (also from external libraries) using different methods of execution to other work using other Thread Pool realizations and to a work without any threading.
For example I have 24 tasks to complete and 10000 random Strings in benchmark state:
...ANSWER
Answered 2021-Oct-29 at 14:03See answers to this question to learn how to write benchmarks in java.
... executorService maybe correct (but i am still unsure) ...
QUESTION
I need your help, there is a livedata that returns a Boolean value that is constantly changing. I need that when true the coroutine is executed (there is just an imitation of loading percentages from 0 to 100%), respectively, false cancels it, and so on in a circle.
if it returns true, ran the coroutine otherwise canceled it
...ANSWER
Answered 2021-Aug-14 at 11:53launch returns a Job, which you can cancel instead of the whole coroutine scope.
So I'd do something as follows:
- Save a reference to your counter job:
private var counterJob: Job? = null - Update it when needed:
counterJob = launch { counter() } - Cancel it when needed:
counterJob?.cancel()
QUESTION
so I'm new to Python and I was looking to remove partially similar entries within the same column. For example these are the entries in one of the columns in a dataframe-
Row 1 - "I have your Body Wash and I wonder if it contains animal ingredients. Also, which animal ingredients? I prefer not to use product with animal ingredients."
Row 2 - "This also doesn't have the ADA on there. Is this a fake toothpaste an imitation of yours?"
Row 3 - "I have your Body Wash and I wonder if it contains animal ingredients. I prefer not to use product with animal ingredients."
Row 4 - "I didn't see the ADA stamp on this box. I just want to make sure it was still safe to use?"
Row 5 - "Hello, I was just wondering if the new toothpaste is ADA approved? It doesn’t say on the packaging"
Row 6 - "Hello, I was just wondering if the new toothpaste is ADA approved? It doesn’t say on the box."
So in this column, rows 1&3, and rows 5&6 are similar (partial duplicates). I want python to recognize these as duplicates, retain the longer sentence and drop the shorter one and export the new data to a csv file.
Expected output - Row 1 - "I have your Body Wash and I wonder if it contains animal ingredients. Also, which animal ingredients? I prefer not to use product with animal ingredients."
Row 2 - "This also doesn't have the ADA on there. Is this a fake toothpaste an imitation of yours?"
Row 3 - "I didn't see the ADA stamp on this box. I just want to make sure it was still safe to use?"
Row 4 - "Hello, I was just wondering if the new toothpaste is ADA approved? It doesn’t say on the packaging"
I tried using FuzzyWuzzy wherein I used the similarity sort function, but it didn't give me the expected output. is there any simpler code for this?
...ANSWER
Answered 2021-Jun-22 at 08:39Here is my approach, hopefully the comments are self explanatory.
QUESTION
I am trying to implement the Generalized Bass Model (GBM) in R with price as a decision variable. Price is decreasing and product adoption is increasing through the years in my dataset. However, I am finding the price sensitivity (alpha) sign as positive in my estimations which is strange because in the literature, the authors find it negative as in (see in Kurdgelashvili et al.). My scaling function is the following as in Kurdgelashvili et al.:
...ANSWER
Answered 2021-Jun-07 at 15:53After contacting some experienced researchers, they told me that in fact my model was suffering from the omitted variable bias and I should use other decision variables. Indeed, when I added other variables, the coeficients signs were as expected.
QUESTION
How would one elegantly take these two inputs:
(def foo [:a 1 :a 1 :a 2])
(def bar [{:hi "there 1"}{:hi "there 2"}{:hi "there 3"}{:hi "there 4"}{:h1 "there 5"}])
and get:
[{:hi "there 1" :a 1}{:hi "there 2" :a 1}{:hi "there 3" :a 2}{:hi "there 4" :a 1}{:hi "there 5" :a 1}]
The first collection cycles at the point the second collection reaches the same number of elements. It would be fine for the first collection to be any of these as it's going to be hard coded:
(def foo [{:a 1} {:a 1} {:a 2}])
(def foo [[:a 1] [:a 1] [:a 2]])
(def foo [1 1 2])
There may be another data structure that would be even better. The 1 1 2 is deliberate as it's not 1 2 3 which would allow range or something like that.
Cycling through the first collection is easy... I'm not sure how to advance through the second collection at the same time. But my approach may not be right in the first place.
As usual, I tend toward weird nested imitations of imperative code but I know there's a better way!
...ANSWER
Answered 2021-Apr-03 at 06:24Here's one way to do it:
You can take the values from foo, cycle through them and partition them in groups of 2 at a time. There's a little secret of vectors of size 2, which is that they can work as a little map (1 key/value pair).
Once we have two collections of maps, we can merge them together. One collection is infinite but that's OK, map will compute only the values until one collection runs out of elements. mapv is the same as map but it returns a vector instead.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install imitation
You can use imitation like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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