multiagent-particle-envs | agent particle environment used in the paper | Reinforcement Learning library

 by   openai Python Version: Current License: MIT

kandi X-RAY | multiagent-particle-envs Summary

kandi X-RAY | multiagent-particle-envs Summary

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

Status: Archive (code is provided as-is, no updates expected).

            kandi-support Support

              multiagent-particle-envs has a medium active ecosystem.
              It has 1868 star(s) with 711 fork(s). There are 155 watchers for this library.
              It had no major release in the last 6 months.
              There are 49 open issues and 31 have been closed. On average issues are closed in 42 days. There are 8 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of multiagent-particle-envs is current.

            kandi-Quality Quality

              multiagent-particle-envs has 0 bugs and 57 code smells.

            kandi-Security Security

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

            kandi-License License

              multiagent-particle-envs 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

              multiagent-particle-envs 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.
              multiagent-particle-envs saves you 724 person hours of effort in developing the same functionality from scratch.
              It has 1671 lines of code, 176 functions and 21 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed multiagent-particle-envs and discovered the below as its top functions. This is intended to give you an instant insight into multiagent-particle-envs implemented functionality, and help decide if they suit your requirements.
            • Set the agent s action .
            • Create an environment for a scenario .
            • Render the scene .
            • Set boundaries .
            • Calculate the action .
            • Compute the collision between two entities .
            • Display an image .
            • Return a pyglet display instance .
            • Make a circle
            • Makes a capule
            Get all kandi verified functions for this library.

            multiagent-particle-envs Key Features

            No Key Features are available at this moment for multiagent-particle-envs.

            multiagent-particle-envs Examples and Code Snippets

            No Code Snippets are available at this moment for multiagent-particle-envs.

            Community Discussions

            Trending Discussions on multiagent-particle-envs


            Random agent on multi-agent gym environments
            Asked 2018-Nov-15 at 12:46

            I'm not able to select random actions for multi-agent gym environments.



            Answered 2018-Nov-15 at 12:46

            Answering my own question, let's consider the simple_tag environment.

            env.action_space for this environment gives:

            [Discrete(5), Discrete(5), Discrete(5), Discrete(5)] (4 agents)

            This is what I found misleading. I thought the actions would have to be a list of 4 elements, something like: [0, 3, 4, 1] but what it expects is a one-hot vector (of 5 elements) for all 4 agents. So, the correct way to encode actions is:

            [array([1., 0., 0., 0., 0.]), array([0., 0., 1., 0., 0.]), array([0., 0., 0., 0., 1.]), array([0., 0., 0., 1., 0.])]

            (depending on the environment)


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


            No vulnerabilities reported

            Install multiagent-particle-envs

            You can download it from GitHub.
            You can use multiagent-particle-envs 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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