reinforcement_learning | side integration library for Reinforcement Learning loops | Machine Learning library

 by   VowpalWabbit C++ Version: 0.2.0 License: MIT

kandi X-RAY | reinforcement_learning Summary

kandi X-RAY | reinforcement_learning Summary

reinforcement_learning is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. reinforcement_learning has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Interaction-side integration library for Reinforcement Learning loops: Predict, Log, [Learn,] Update
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              reinforcement_learning has a low active ecosystem.
              It has 65 star(s) with 41 fork(s). There are 15 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 49 open issues and 24 have been closed. On average issues are closed in 244 days. There are 12 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of reinforcement_learning is 0.2.0

            kandi-Quality Quality

              reinforcement_learning has no bugs reported.

            kandi-Security Security

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

            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.
              Installation instructions, examples and code snippets are available.

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            reinforcement_learning Key Features

            No Key Features are available at this moment for reinforcement_learning.

            reinforcement_learning Examples and Code Snippets

            reinforcement_learning,Ubuntu,Dependencies
            C++dot img1Lines of Code : 27dot img1License : Permissive (MIT)
            copy iconCopy
            sudo apt-get install libboost-all-dev libssl-dev
            
            cd ~
            git clone https://github.com/Microsoft/cpprestsdk.git cpprestsdk
            cd cpprestsdk
            # Checkout 2.10.1 version of cpprestsdk
            git checkout e8dda215426172cd348e4d6d455141f40768bf47
            git submodule update -  
            reinforcement_learning,Troubleshooting
            C++dot img2Lines of Code : 26dot img2License : Permissive (MIT)
            copy iconCopy
            Make Error at /usr/local/Cellar/cmake/3.14.4/share/cmake/Modules/FindPackageHandleStandardArgs.cmake:137 (message):
              Could NOT find OpenSSL, try to set the path to OpenSSL root folder in the
              system variable OPENSSL_ROOT_DIR (missing: OPENSSL_INCLU  
            reinforcement_learning,Ubuntu,Configure + Build
            C++dot img3Lines of Code : 6dot img3License : Permissive (MIT)
            copy iconCopy
            cmake -S . -B build
            cmake --build build --target all -j 4 # $(nproc)
            
            # Test
            cmake --build build --target rltest -j 4 # $(nproc)
            cmake --build build --target test
              

            Community Discussions

            QUESTION

            ValueError: Tape is still recording, This can happen if you try to re-enter an already-active tape
            Asked 2021-Jan-15 at 12:05

            I write some tensorflow code about Deep Successor Representation (DSQ) reinforcement learning:

            ...

            ANSWER

            Answered 2021-Jan-15 at 08:07

            A call to the optimizer must be out of the scope of the gradient tape, i.e:

            Source https://stackoverflow.com/questions/65732431

            QUESTION

            Reinforcement Learning coach : Saver fails to restore agent's checkpoint
            Asked 2020-Oct-17 at 11:54

            I'm using rl coach through AWS Sagemaker, and I'm running in an issue that I struggle to understand.

            I'm performing RL using AWS Sagemaker for the learning, and AWS Robomaker for the environment, like in DeepRacer which uses rl coach as well. In fact, the code only little differs with the DeepRacer code on the learning side. But the environment is completely different though.

            What happens:

            • The graph manager initialization succeeds
            • A first checkpoint is generated (and uploaded to S3)
            • The agent loads the first checkpoint
            • The agent performs N episodes with the first policy
            • The graph manager fetches the N episodes
            • The graph manager performs 1 training step and create a second checkpoint (uploaded to S3)
            • The agent fails to restore the model with the second checkpoint.

            The agent raises an exception with the message : Failed to restore agent's checkpoint: 'main_level/agent/main/online/global_step'

            The traceback points to a bug happening in this rl coach module:

            ...

            ANSWER

            Answered 2020-Oct-17 at 11:54

            I removed the patch (technically I removed the patch command in my dockerfile that was applying it), and now it works, the model is correctly restored from the checkpoint.

            Source https://stackoverflow.com/questions/64349126

            QUESTION

            How to make the inputs and model have the same shape (RLlib Ray Sagemaker reinforcement learning)
            Asked 2019-Sep-18 at 20:19

            I have a mismatch in shapes between inputs and the model of my reinforcement learning project.

            I have been closely following the AWS examples, specifically the cartpole example. However I have built my own custom environment. What I am struggling to understand is how to change my environment so that it is able to work with the prebuilt Ray RLEstimator.

            Here is the code for the environment:

            ...

            ANSWER

            Answered 2019-Sep-18 at 20:19

            Possible reason:

            The error message:

            ValueError: Input 0 of layer default/fc1 is incompatible with the layer: : expected min_ndim=2, found ndim=1. Full shape received: [None]

            Your original environment obs space is self.observation_space = Box(np.array(0.0),np.array(1000)).

            Displaying the shape of your environment obs space gives:

            print(Box(np.array(0.0), np.array(1000), dtype=np.float32).shape) = ()

            This could be indicated by Full shape received: [None] in the error message.

            If you pass the shape (1,1) into np.zeros, you get the expected min_ndim=2:

            x = np.zeros((1, 1)) print(x) [[0.]] print(x.ndim) 2

            Suggested solution:

            I assume that you want your environment obs space to range from 0.0 to 1000.0 as indicated by the self.price = np.random.rand() in your reset function.

            Try using the following for your environment obs space:

            self.observation_space = Box(0.0, 1000.0, shape=(1,1), dtype=np.float32)

            I hope that by setting the Box with an explicit shape helps.

            EDIT (20190903):

            I have modified your training script. This modification includes new imports, custom model class, model registration & addition of registered custom model to config. For readability, only sections added are shown below. The entire modified training script is available in this gist. Please run with the proposed obs space as describe above.

            New additional imports:

            Source https://stackoverflow.com/questions/57724414

            QUESTION

            How to look at the parameters of a pytorch model?
            Asked 2019-Feb-27 at 00:10

            I have a simple pytorch neural net that I copied from openai, and I modified it to some extent (mostly the input).

            When I run my code, the output of the network remains the same on every episode, as if no training occurs.

            I want to see if any training happens, or if some other reason causes the results to be the same.

            How can I make sure any movement happens to the weights?

            Thanks

            ...

            ANSWER

            Answered 2019-Feb-27 at 00:10

            Depends on what you are doing, but the easiest would be to check the weights of your model.

            You can do this (and compare with the ones from previous iteration) using the following code:

            Source https://stackoverflow.com/questions/54259943

            QUESTION

            Pytorch ValueError: optimizer got an empty parameter list
            Asked 2019-Feb-14 at 06:29

            When trying to create a neural network and optimize it using Pytorch, I am getting

            ValueError: optimizer got an empty parameter list

            Here is the code.

            ...

            ANSWER

            Answered 2019-Feb-14 at 06:29

            Your NetActor does not directly store any nn.Parameter. Moreover, all other layers it eventually uses in forward are stored as a simple list is self.nn_layers.
            If you want self.actor_nn.parameters() to know that the items stored in the list self.nn_layers may contain trainable parameters, you should work with containers.
            Specifically, making self.nn_layers to be a nn.ModuleList instead of a simple list should solve your problem:

            Source https://stackoverflow.com/questions/54678896

            QUESTION

            What's the difference between torch.stack() and torch.cat() functions?
            Asked 2019-Jan-22 at 11:31

            OpenAI's REINFORCE and actor-critic example for reinforcement learning has the following code:

            REINFORCE:

            ...

            ANSWER

            Answered 2019-Jan-22 at 11:31

            stack

            Concatenates sequence of tensors along a new dimension.

            cat

            Concatenates the given sequence of seq tensors in the given dimension.

            So if A and B are of shape (3, 4), torch.cat([A, B], dim=0) will be of shape (6, 4) and torch.stack([A, B], dim=0) will be of shape (2, 3, 4).

            Source https://stackoverflow.com/questions/54307225

            QUESTION

            OpenAI Integrating custom game into a gym environment
            Asked 2018-Apr-05 at 17:58

            [Introduction] I'm a beginner with OpenAI, I have made a custom game into which I would like to implement a self-learning agent. I followed this guide to set up a repository on GitHub, however I do not understand how I could format my code to work with the contents of gym-foo/gym_foo/envs/foo_env.py

            [Question] Is there any chance someone could guide me on how to structure my code to so it’s compatible with:

            ...

            ANSWER

            Answered 2018-Apr-05 at 17:58

            I have no experience with the pygame library and no knowledge of its internal workings, that may have some influence on what code needs to run where, so I'm not 100% sure on all of that. But, it's good to just start with some intuitive understanding of roughly what should be happening where:

            • __init__() should run any one-time setup. I can imagine something like pygame.init() may have to go in here, but this I'm not 100% sure on because I'm not familiar with pygame.
            • step() should be called whenever an agent selects an action, and then run a single ''frame'' of the game, move it forwards given the action selected by the agent. Alternatively, if you have a game where a single action takes multiple frames, you should run multiple frames here. Essentially: keep the game moving forwards until you hit a point where the agent should get to choose a new action again, then return the current game state.
            • reset() should... well, reset the game. So, revert back to the (or a random, whatever you want) initial game state, run any cleanup that may be required. I could, for example, also imagine pygame.init() belonging in here. It depends on what exactly that function does. If it only needs to be run once, it belongs in __init__(). If it needs to run at the start of every new game/"episode", ir belongs in reset().
            • render() should probably contain most of your graphics related code. You can try to take inspiration from, for example, the cartpole environment in gym, which also draws some rather simple graphics here. It looks like it should draw exactly one frame.

            Now, looking at the code you're starting from, there seems to be a signifant amount of User Interface code... all kinds of code related to buttons, pausing/unpausing, a fancy (animated?) intro at the start of the game. I don't know if you can afford to get rid of all this? If you're doing purely Reinforcement Learning, you probably can. If you still need user interaction, you probably can't, and then things become a whole lot more difficult since all these things do not nicely fit the gym framework.

            I can try to make a few educated guesses of a few of the remaining parts of the code and where it should go, but you should carefully inspect everything anyway based on the more general guidelines above:

            Source https://stackoverflow.com/questions/49637378

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

            Vulnerabilities

            No vulnerabilities reported

            Install reinforcement_learning

            In order to build using homebrew dependencies, you must invoke cmake this way:.

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            https://github.com/VowpalWabbit/reinforcement_learning.git

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            gh repo clone VowpalWabbit/reinforcement_learning

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            git@github.com:VowpalWabbit/reinforcement_learning.git

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