reinforcement-learning | Minimal and Clean Reinforcement Learning Examples | Reinforcement Learning library

 by   rlcode Python Version: Current License: MIT

kandi X-RAY | reinforcement-learning Summary

kandi X-RAY | reinforcement-learning Summary

reinforcement-learning is a Python library typically used in Artificial Intelligence, Reinforcement Learning, Deep Learning applications. reinforcement-learning has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can download it from GitHub.

Minimal and clean examples of reinforcement learning algorithms presented by RLCode team. [한국어] Maintainers - Woongwon, Youngmoo, Hyeokreal, Uiryeong, Keon.

            kandi-support Support

              reinforcement-learning has a highly active ecosystem.
              It has 3150 star(s) with 724 fork(s). There are 125 watchers for this library.
              It had no major release in the last 6 months.
              There are 25 open issues and 28 have been closed. On average issues are closed in 116 days. There are 10 open pull requests and 0 closed requests.
              It has a positive sentiment in the developer community.
              The latest version of reinforcement-learning is current.

            kandi-Quality Quality

              reinforcement-learning has 0 bugs and 0 code smells.

            kandi-Security Security

              reinforcement-learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              reinforcement-learning code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              reinforcement-learning is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              reinforcement-learning releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              reinforcement-learning saves you 1574 person hours of effort in developing the same functionality from scratch.
              It has 3502 lines of code, 258 functions and 30 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed reinforcement-learning and discovered the below as its top functions. This is intended to give you an instant insight into reinforcement-learning implemented functionality, and help decide if they suit your requirements.
            • Train the model
            • Returns n random samples from the tree
            • Retrieve an item from the queue
            • Internal method to retrieve an index from the tree
            • Move the circle
            • Convert coordinates to state coordinates
            • Check if the reward is in the given state
            • Gets the state of the agent
            • Convert i to np array
            • Return the action given a state
            • Return the action of the given state
            • Resets the widget to the current state
            • Print the optimal policy
            • Calculate action
            • Calculate the action given the history
            • Learn the given action
            • Calculates the value of all texts
            • Calculate action based on q function
            • Train the replay
            • Gets the action list for the given state
            • Move the rectangle by policy
            • Train a replay
            • Print the text value of the QTable
            • Returns the action corresponding to the given state
            • Add data to the accumulator
            • Update the model
            Get all kandi verified functions for this library.

            reinforcement-learning Key Features

            No Key Features are available at this moment for reinforcement-learning.

            reinforcement-learning Examples and Code Snippets

            Reinforcement Learning-Running Reinforcement Learning
            Pythondot img1Lines of Code : 80dot img1License : Strong Copyleft (GPL-3.0)
            copy iconCopy
            freqtrade trade --freqaimodel ReinforcementLearner --strategy MyRLStrategy --config config.json
                def populate_any_indicators(
                    self, pair, df, tf, informative=None, set_generalized_indicators=False
                    if informative is None:
            Reinforcement Learning-Creating a custom reward function
            Pythondot img2Lines of Code : 67dot img2License : Strong Copyleft (GPL-3.0)
            copy iconCopy
                from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
                from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
                class MyCoolRLModel(ReinforcementLearner):
            copy iconCopy
            'Ball3DAcademy' started successfully!
            Unity Academy name: Ball3DAcademy
            INFO:mlagents_envs:Connected new brain:
            Unity brain name: 3DBallLearning
                    Number of Visual Observations (per agent): 0
                    Vector Observation spac  
            Noisy linear cosine decay .
            pythondot img4Lines of Code : 92dot img4License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def noisy_linear_cosine_decay(learning_rate,
            Linear cosine decay .
            pythondot img5Lines of Code : 82dot img5License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def linear_cosine_decay(learning_rate,
            Sigmoid activation function .
            pythondot img6Lines of Code : 47dot img6License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def swish(features, beta=1.0):
              # pylint: disable=g-doc-args
              """Computes the SiLU or Swish activation function: `x * sigmoid(beta * x)`.
              beta : Hyperparameter for Swish activation function. Default value 1.0.
              The SiLU activation function was  

            Community Discussions


            What is wrong with my simple code in importing a module?
            Asked 2021-Jul-28 at 18:06

            I have written the simple code below to check how to import a module:



            Answered 2021-Jul-28 at 18:06

            Python automatically "binds" the first argument of a method that was declared in a class, to the instance of an object it was called on. So you have to instantiate your Test class (i.e., create a new Test) in order to get this behavior. You can also just call it directly, but in this case, you have to provide the value of self yourself (heh).



            Why is this tensorflow training taking so long?
            Asked 2021-May-13 at 12:42

            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:



            Answered 2021-May-13 at 12:42
            Why is TensorFlow slow

            TensorFlow 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.function works 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:

            Applying tf.function to your code

            So, there are at least two things you can change in your code to make it run quite faster:

            1. The first one is to not use model.predict on 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 a tf.function.

            Model.predict is a top-level API designed for batch-predicting outside of any loops, with the fully-features of the Keras APIs.

            1. 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:



            module 'tensorflow' has no attribute 'tanh'
            Asked 2020-Sep-23 at 23:08

            I am trying to repeat what is shown in this tutorial:

            When I run this code:



            Answered 2020-Sep-23 at 23:08

            I fixed the problem by specifying TensorFlow version:



            Appending a list to another list in python
            Asked 2020-Aug-08 at 20:11

            I have a .txt file that contains data like this :



            Answered 2020-Aug-08 at 13:29


            Visualizing custom loss in double-head model
            Asked 2020-Apr-29 at 08:41

            Using an A2C agent from this article, how to get numerical values of value_loss, policy_loss and entropy_loss when weights are being updated?

            The model I'm using is double-headed, both heads share the same trunk. The policy head output shape is [number of actions, batch size] and value head has a shape of [1, batch_size]. Compiling this model returns a size incompatibility error, when these loss functions are given as metrics:



            Answered 2020-Apr-29 at 08:41

            I found the answer to my problem. In Keras, the metrics built-in functionality provides an interface for measuring performance and losses of the model, be it a custom or standard one.

            When compiling a model as follows:



            How are n dimensional vectors state vectors represented in Q Learning?
            Asked 2020-Apr-16 at 07:47

            Using this code:



            Answered 2020-Apr-16 at 07:47

            Yes, states can be represented by anything you want, including vectors of arbitrary length. Note, however, that if you are using a tabular version of Q-learning (or SARSA as in this case), you must have a discrete set of states. Therefore, you need a way to map the representation of your state (for example, a vector of potentially continuous values) to a set of discrete states.

            Expanding on the example you have given, imagine that you have three states represented by vectors:



            Tensorflow var does not exists error and resource not found error
            Asked 2020-Apr-09 at 12:47

            I am trying to run the code given by sentdex mentioned in


            Windows 10, Carla 0.9.5, Python 3.7.5, Tensorflow 1.14.0.

            I am not using any GPU version of the tensorflow. I have made few changes in the imports of the code. When I am running this code I am getting Resource not found error. Also note that an instance of carla is already running in background at port 2000 as mentioned in the code. Till now I have played with the imports by changing the locations of them along with installing different versions of tensorflow.

            Changes in import:-



            Answered 2020-Mar-01 at 15:21

            After many days of research I found out that the resources were not getting initialized and hence it was saying var does not exist. So, I found few lines of codes that I pasted after graph.as_default() and before saving the model. These are the lines of codes:-



            reinforcement learning - number of actions
            Asked 2020-Mar-14 at 12:18


            Answered 2020-Mar-14 at 12:18

            Yes, you are right. Usually you define a dictionary containing a map between integers and every action your agent can make. You can see that in the function n_actions is used exactly to sample a random action index when you don't select the optimal one.



            What does the notation self(x) do?
            Asked 2020-Jan-09 at 16:45

            I'm learning about Distributional RL from 'Deep Reinforcement Learning Hands On' code. And there is a method in model class:



            Answered 2020-Jan-09 at 16:45

            It will call the __call__ method on the instance. See this demo:


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


            No vulnerabilities reported

            Install reinforcement-learning

            You can download it from GitHub.
            You can use reinforcement-learning 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.


            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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