pytorch-ddpg | Deep Deterministic Policy Gradient using PyTorch | Reinforcement Learning library

 by   ghliu Python Version: Current License: Apache-2.0

kandi X-RAY | pytorch-ddpg Summary

kandi X-RAY | pytorch-ddpg Summary

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

Implementation of the Deep Deterministic Policy Gradient (DDPG) using PyTorch
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            kandi-support Support

              pytorch-ddpg has a low active ecosystem.
              It has 458 star(s) with 147 fork(s). There are 8 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 4 open issues and 7 have been closed. On average issues are closed in 14 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of pytorch-ddpg is current.

            kandi-Quality Quality

              pytorch-ddpg has 0 bugs and 17 code smells.

            kandi-Security Security

              pytorch-ddpg has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              pytorch-ddpg code analysis shows 0 unresolved vulnerabilities.
              There are 1 security hotspots that need review.

            kandi-License License

              pytorch-ddpg 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.

            kandi-Reuse Reuse

              pytorch-ddpg releases are not available. You will need to build from source code and install.
              pytorch-ddpg has no build file. You will be need to create the build yourself to build the component from source.
              pytorch-ddpg saves you 245 person hours of effort in developing the same functionality from scratch.
              It has 596 lines of code, 67 functions and 8 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed pytorch-ddpg and discovered the below as its top functions. This is intended to give you an instant insight into pytorch-ddpg implemented functionality, and help decide if they suit your requirements.
            • Train an agent
            • Save the model
            • Observe current state
            • Generate a random action
            • Evaluate the given model
            • Load weights
            • Evaluate the model
            • Finalize an episode
            • Append an observation
            • Get the output folder for a given environment
            • Get the most recent observation
            • Sample from the model
            • Sets up a tensor
            Get all kandi verified functions for this library.

            pytorch-ddpg Key Features

            No Key Features are available at this moment for pytorch-ddpg.

            pytorch-ddpg Examples and Code Snippets

            No Code Snippets are available at this moment for pytorch-ddpg.

            Community Discussions

            QUESTION

            Calling .backward() function for two different neural networks but getting retain_graph=True error
            Asked 2021-Jan-20 at 20:00

            I have an Actor Critic neural network where the Actor is its own class and the Critic is its own class with its own neural network and .forward() function. I then am creating an object of each of these classes in a larger Model class. My setup is as follows:

            ...

            ANSWER

            Answered 2021-Jan-20 at 19:09

            Yes, you shouldn't do it like that. What you should do instead, is propagating through parts of the graph.

            What the graph contains

            Now, graph contains both actor and critic. If the computations pass through the same part of graph (say, twice through actor), it will raise this error.

            • And they will, as you clearly use actor and critic joined with loss value (this line: loss_actor = -self.critic(state, action))

            • Different optimizers do not change anything here, as it's backward problem (optimizers simply apply calculated gradients onto models)

            Trying to fix it
            • This is how to fix it in GANs, but not in this case, see Actual fix paragraph below, read on if you are curious about the topic

            If part of a neural network (critic in this case) does not take part in the current optimization step, it should be treated as a constant (and vice versa).

            To do that, you could disable gradient using torch.no_grad context manager (documentation) and set critic to eval mode (documentation), something along those lines:

            Source https://stackoverflow.com/questions/65815598

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

            Vulnerabilities

            No vulnerabilities reported

            Install pytorch-ddpg

            You can download it from GitHub.
            You can use pytorch-ddpg 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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