reinforce | Simple reinforcement learning in Python | Reinforcement Learning library
kandi X-RAY | reinforce Summary
kandi X-RAY | reinforce Summary
A 'plug and play' reinforcement learning library in Python. Infers a Markov Decision Process from data and solves for the optimal policy. Implementation based on Andrew Ng's notes.
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Top functions reviewed by kandi - BETA
- Train the model
- Compute the policy for the given function
- Computes the average rewards for each observation
- Calculate probability probabilities for each state
- Count the number of transition states
- Parse an encoded policy
- Computes the probability of transition probabilities
reinforce Key Features
reinforce Examples and Code Snippets
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jshell> void sayHelloWorldThrice() {
...> System.out.println("Hello World");
...> System.out.println("Hello World");
...> System.out.println("Hello World");
...> }
| created method sayHelloWorldThrice()
jshell> Community Discussions
Trending Discussions on reinforce
QUESTION
The model I used to collect tweets from API V1.1 was like this:
...ANSWER
Answered 2021-Jun-09 at 21:10The structure of responses differ and you need to actually modify a little for your v2.
Update your loop condition.
Modifications:obj_datain v2 contains additionalmeta, thus you need to get the length ofobj_data.datainstead of justobj_data
QUESTION
I'm currently working on a Deep reinforcement learning problem, and I'm using the categorical distribution to help the agent get random action. This is the code.
...ANSWER
Answered 2021-Jun-09 at 13:53If you look at your probabilities for sampling probs, you see that the 1th class has the largest probability, and almost all others are < 1%. If you are not familiar with scientific notation, here it is formatted as rounded percentages:
QUESTION
I'm building a reinforcement learning library where I'd like to pass certain instance information into the executables via a piped JSON.
Using aeson's Simplest.hs, I'm able to get the following basic example working as intended. Note that the parameters are sitting in Main.hs as a String params as a placeholder.
I tried to modify Main.hs so I would pipe the Nim game parameters in from a JSON file via getContents, but am running into the expected [Char] vs. IO String issue. I've tried to read up as much as possible about IO, but can't figure out how to lift my JSON parsing method to deal with IO.
How would I modify the below so that I can work with piped-in JSON?
Main.hs
...ANSWER
Answered 2021-Jun-08 at 01:17getContents returns not a String as you apparently expect, but IO String, which is a "program", which, when executed, will produce a String. So when you're trying to parse this program with decode, of course that doesn't work: decode parses a String, it cannot parse a program.
So how do you execute this program to obtain the String? There are two ways: either you make it part of another program or you call it main and it becomes your entry point.
In your case, the sensible thing to do would be to make getContent part of your main program. To do that, use the left arrow <-, like this:
QUESTION
I have a dynamically allocated array that contains data for a piece of land:
...ANSWER
Answered 2021-May-27 at 10:47as far as I understand, the direction is important for you. if it's, then the following changing is enough for that.
QUESTION
I am still relatively new to the Unity environment and am currently working with reinforcement learning and ML agents. For this I wanted to add an agent to the 2D platformer.
I have attached two ray perception sensors to my agent. Unfortunately I can't get any hits with these sensors, at least they are not displayed as usual with a sphere in the gizmos.
The sensors are casting rays, but like you see in the image, they are not colliding.
The ray perception sensor are childs of the agent, defined in its prefab. I defined the sensors to collide with 4 tags: Untagged, ground, enemy and coin
I assigned the coin tag to the token, the enemy tag to the enemy and the ground tag to the tilemap forming the ground. The token has a circle collider, while the enemy has an capsule collider. On the tilemap there is a tilmap collider.
I would now expect the sensor to collide with the token, enemy and ground and display these hits in spheres, but it does not.
So, what am I doing wrong?
ANSWER
Answered 2021-May-24 at 13:49After a lot more investigation i figured out the problem myself:
The tags where correctly configured, but i had an misunderstanding in the Ray Layer Mask.
Previously i had configured it to "Everything"/"Default" which resulted in a collision in the sensor itself and seems not right (Despite the player tag was not in the detagtable tags).
After i created more layers and assigned my targets to these layers, everything starts working as intended.
Maybe this answer will help someone, having similar issues.
QUESTION
I'm struggling with a python class method returning a NoneType value instead of the desired object. I'm working with reinforcement learning a need to create a 2D map of tiles but my get_tile method keeps return NoneType
...ANSWER
Answered 2021-May-21 at 05:33You need to use and instead of &
QUESTION
I am trying to get the second last value in each row of a data frame, meaning the first job a person has had. (Job1_latest is the most recent job and people had a different number of jobs in the past and I want to get the first one). I managed to get the last value per row with the code below:
first_job <- function(x) tail(x[!is.na(x)], 1)
first_job <- apply(data, 1, first_job)
...ANSWER
Answered 2021-May-11 at 13:56You can get the value which is next to last non-NA value.
QUESTION
I in my React app I send a request containing a JSON body with various fields, among which is an array of File type images.
ANSWER
Answered 2021-May-13 at 14:37You cannot use json to send images(not the original file), but theres an option with form data. Firts, you have to separate the files from the normal data, and parse the normal data to a string.
QUESTION
I'm learning DRL with the book Deep Reinforcement Learning in Action. In chapter 3, they present the simple game Gridworld (instructions here, in the rules section) with the corresponding code in PyTorch.
I've experimented with the code and it takes less than 3 minutes to train the network with 89% of wins (won 89 of 100 games after training).
As an exercise, I have migrated the code to tensorflow. All the code is here.
The problem is that with my tensorflow port it takes near 2 hours to train the network with a win rate of 84%. Both versions are using the only CPU to train (I don't have GPU)
Training loss figures seem correct and also the rate of a win (we have to take into consideration that the game is random and can have impossible states). The problem is the performance of the overall process.
I'm doing something terribly wrong, but what?
The main differences are in the training loop, in torch is this:
...ANSWER
Answered 2021-May-13 at 12:42TensorFlow has 2 execution modes: eager execution, and graph mode. TensorFlow default behavior, since version 2, is to default to eager execution. Eager execution is great as it enables you to write code close to how you would write standard python. It's easier to write, and it's easier to debug. Unfortunately, it's really not as fast as graph mode.
So the idea is, once the function is prototyped in eager mode, to make TensorFlow execute it in graph mode. For that you can use tf.function. tf.function compiles a callable into a TensorFlow graph. Once the function is compiled into a graph, the performance gain is usually quite important. The recommended approach when developing in TensorFlow is the following:
- Debug in eager mode, then decorate with
@tf.function.- Don't rely on Python side effects like object mutation or list appends.
tf.functionworks best with TensorFlow ops; NumPy and Python calls are converted to constants.
I would add: think about the critical parts of your program, and which ones should be converted first into graph mode. It's usually the parts where you call a model to get a result. It's where you will see the best improvements.
You can find more information in the following guides:
Applyingtf.function to your code
So, there are at least two things you can change in your code to make it run quite faster:
- The first one is to not use
model.predicton a small amount of data. The function is made to work on a huge dataset or on a generator. (See this comment on Github). Instead, you should call the model directly, and for performance enhancement, you can wrap the call to the model in atf.function.
Model.predict is a top-level API designed for batch-predicting outside of any loops, with the fully-features of the Keras APIs.
- The second one is to make your training step a separate function, and to decorate that function with
@tf.function.
So, I would declare the following things before your training loop:
QUESTION
I have been learning Reinforcement Learning for few days now, and I have seen example problems like Mountain Car problem and Cart Pole problem.
In these problems, the way action space is described is discrete. For example in Cart Pole Problem, the agent can either move left or move right.
But the examples don't talk about how much? How does the agent decide how much to move left, how much to move right, after all these movements are continuous space actions. So I want to know how does the agent decide what real value to choose from a continuous action space.
Also I have been using ReinforcementLearning.jl in Julia and wanted to know a way i could represent range constraints on action space in it. Example, the real value that the agent chooses as it's action should lie in a range like [10.00, 20.00[ for example. I want to know how this can be done.
ANSWER
Answered 2021-May-11 at 07:27
- But the examples don't talk about how much? How does the agent decide how much to move left, how much to move right, after all these movements are continuous space actions. So I want to know how does the agent decide what real value to choose from a continuous action space.
The common solution is to assume that the output of the agent follows the normal distribution. Then you only need to design an agent that predicts the mean and std. Finally sample a random action from that distribution and pass it to the environment.
Another possible solution is to discretize the continuous action space and turn it into a discrete action space problem. Then randomly sample one action from the predicted bin.
- Also I have been using ReinforcementLearning.jl in Julia and wanted to know a way i could represent range constraints on action space in it. Example, the real value that the agent chooses as it's action should lie in a range like [10.00, 20.00[ for example. I want to know how this can be done.
You can take a look at the implementation detail of the PendulumEnv. Currently, it uses .. from IntervalSets.jl to describe a continuous range.
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Install reinforce
You can use reinforce 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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