ReinforcementLearning | Reinforcing Your Learning of Reinforcement | Reinforcement Learning library

 by   Urinx Python Version: Current License: Apache-2.0

kandi X-RAY | ReinforcementLearning Summary

kandi X-RAY | ReinforcementLearning Summary

ReinforcementLearning is a Python library typically used in Artificial Intelligence, Reinforcement Learning, Pytorch applications. ReinforcementLearning has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However ReinforcementLearning build file is not available. You can download it from GitHub.

Reinforcing Your Learning of Reinforcement Learning
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              ReinforcementLearning has a low active ecosystem.
              It has 65 star(s) with 12 fork(s). There are 5 watchers for this library.
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              It had no major release in the last 6 months.
              ReinforcementLearning has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of ReinforcementLearning is current.

            kandi-Quality Quality

              ReinforcementLearning has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              ReinforcementLearning 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.

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              ReinforcementLearning releases are not available. You will need to build from source code and install.
              ReinforcementLearning has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed ReinforcementLearning and discovered the below as its top functions. This is intended to give you an instant insight into ReinforcementLearning implemented functionality, and help decide if they suit your requirements.
            • Train an environment
            • Print the state
            • Preprocess a frame
            • Create a game
            • Stack a frame of frames
            • Play a game
            • Returns the winner of the player
            • Checks if the player is in the player
            • Move the board
            • Build the model
            • Resolve a residual layer
            • Apply policy head layer
            • Convolution layer
            • Create a test environment
            • Sample a batch of observations
            • Play the game
            • Test environment
            • Return a random action
            • Set the reward function
            • Compute the logarithm of the model
            • Prints the current state
            • Record loss and value head loss
            Get all kandi verified functions for this library.

            ReinforcementLearning Key Features

            No Key Features are available at this moment for ReinforcementLearning.

            ReinforcementLearning Examples and Code Snippets

            No Code Snippets are available at this moment for ReinforcementLearning.

            Community Discussions

            QUESTION

            Multidimensional Action Space in Reinforcement Learning
            Asked 2022-Apr-17 at 22:05

            My goal is to train an agent (ship) that takes two actions for now. 1. Choosing it's heading angle (where to go next) and 2. Choosing it's acceleration (if it will change its speed or not).

            However, it seems like that I cannot undestand how to properly construct my action space and state space. I keep getting an error which I do not know how to fix. I have been trying to make it work using the Space wrapper.

            I use the following code.

            ...

            ANSWER

            Answered 2022-Apr-17 at 15:05

            I think the error message already explained it clearly.

            Source https://stackoverflow.com/questions/71901031

            QUESTION

            Using neural network approximator in reinforcementlearning.jl
            Asked 2021-May-14 at 07:41

            I am trying to create a simultaneous multi agent environment using reinforcementlearning.jl
            I have successfully represented the environment and it works with a RandomPolicy for every agent.

            But my state space is large (actually it's a 14 tuple with each value in a certain range). So I can not use Tabular Approximators to estimate the Q or V values. That's why I have decided to use a Neural Network Approximator. But the docs do not discuss much about it, nor are there any examples were neural network approximator is used. I am stuck how to figure out how to use such approximator. If anyone can explain how to go about it, or refer to any example, it would be helpful.

            Moreover I found from docs that using a Neural Network approximator needs us to use a CircularArraySARTTrajectory. But defining this trajectory requires a key word argument called capacity. I don't know what it means, nor it is discussed about in the docs and GitHub.

            I tried writing the code that uses neural network approximator but I get error.

            ...

            ANSWER

            Answered 2021-May-14 at 07:41

            Here the capacity means the maximum length of the experience replay buffer. When applying DQN related algorithms, we usually use a circular buffer to store transitions at each step.

            The error you posted above means that you forget to define the size of the state when defining the CircularArraySARTTrajectory.

            Source https://stackoverflow.com/questions/67517535

            QUESTION

            Continuous action spaces in Reinforcement Learning - How does the agent choose action value from a continuous space?
            Asked 2021-May-11 at 07:27

            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.

            1. 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.

            2. 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
            1. 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.

            1. 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.

            Source https://stackoverflow.com/questions/67466527

            QUESTION

            How are input tensors with different shapes fed to neural network?
            Asked 2021-Feb-22 at 11:44

            I am following this tutorial on Policy Gradient using Keras, and can't quite figure out the below.

            In the below case, how exactly are input tensors with different shapes fed to the model?
            Layers are neither .concated or .Added.

            • input1.shape = (4, 4)
            • input2.shape = (4,)
            • "input" layer has 4 neurons, and accepts input1 + input2 as 4d vector??

            The code excerpt (modified to make it simpler) :

            ...

            ANSWER

            Answered 2021-Feb-22 at 11:44

            In cases where you might want to figure out what type of graph you have just build, it is helpful to use the model.summary() or tf.keras.utils.plot_model() methods for debugging:

            Source https://stackoverflow.com/questions/66311025

            QUESTION

            TypeError: an integer is required when using Cython
            Asked 2020-Nov-22 at 13:01

            I am working with Cython to speed up some python code and I am running into the following error:

            ...

            ANSWER

            Answered 2020-Nov-22 at 13:01

            The problem lies in this part of your code:

            Source https://stackoverflow.com/questions/64954318

            QUESTION

            RuntimeError: the derivative for 'indices' is not implemented
            Asked 2020-Jun-06 at 09:40

            I am following this online tutorial for coding a DQN,https://github.com/philtabor/Youtube-Code-Repository/blob/master/ReinforcementLearning/DeepQLearning/torch_deep_q_model.py , however I am running into this Runtime Error that I am unsure of how to debug or modify to prevent this error. Thanks!

            ...

            ANSWER

            Answered 2020-Jun-06 at 09:40

            You have to do use .detach() for :

            Source https://stackoverflow.com/questions/62126327

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

            Vulnerabilities

            No vulnerabilities reported

            Install ReinforcementLearning

            You can download it from GitHub.
            You can use ReinforcementLearning 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.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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