stable-baselines3 | PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms | Reinforcement Learning library

 by   DLR-RM Python Version: 2.4.0a1 License: MIT

kandi X-RAY | stable-baselines3 Summary

kandi X-RAY | stable-baselines3 Summary

stable-baselines3 is a Python library typically used in Artificial Intelligence, Reinforcement Learning, Deep Learning, Pytorch applications. stable-baselines3 has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install stable-baselines3' or download it from GitHub, PyPI.

Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines. You can read a detailed presentation of Stable Baselines3 in the v1.0 blog post or our JMLR paper. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. We also hope that the simplicity of these tools will allow beginners to experiment with a more advanced toolset, without being buried in implementation details. Note: despite its simplicity of use, Stable Baselines3 (SB3) assumes you have some knowledge about Reinforcement Learning (RL). You should not utilize this library without some practice. To that extent, we provide good resources in the documentation to get started with RL.

            kandi-support Support

              stable-baselines3 has a medium active ecosystem.
              It has 5890 star(s) with 1253 fork(s). There are 55 watchers for this library.
              There were 7 major release(s) in the last 6 months.
              There are 63 open issues and 1041 have been closed. On average issues are closed in 19 days. There are 12 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of stable-baselines3 is 2.4.0a1

            kandi-Quality Quality

              stable-baselines3 has 0 bugs and 0 code smells.

            kandi-Security Security

              stable-baselines3 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              stable-baselines3 code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              stable-baselines3 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

              stable-baselines3 releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              stable-baselines3 saves you 4124 person hours of effort in developing the same functionality from scratch.
              It has 11746 lines of code, 956 functions and 93 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed stable-baselines3 and discovered the below as its top functions. This is intended to give you an instant insight into stable-baselines3 implemented functionality, and help decide if they suit your requirements.
            • Evaluate a policy .
            • Samples transitions from the history .
            • Load a BaseAlgorithm from a zip file .
            • Collect rollouts .
            • Load tensor from zip file .
            • Create a VecEnv instance .
            • Stores a transition .
            • Checks the gym .
            • Train the policy .
            • Convert a dictionary to JSON .
            Get all kandi verified functions for this library.

            stable-baselines3 Key Features

            No Key Features are available at this moment for stable-baselines3.

            stable-baselines3 Examples and Code Snippets

            SpaceRobotEnv,Quick Start
            Pythondot img1Lines of Code : 25dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            import gym
            import SpaceRobotEnv
            import numpy as np
            env = gym.make("SpaceRobotState-v0")
            dim_u = env.action_space.shape[0]
            dim_o = env.observation_space["observation"].shape[0]
            observation = env.reset()
            max_action = env.  
            Pythondot img2Lines of Code : 10dot img2License : Permissive (MIT)
            copy iconCopy
            $ cd gym-pybullet-drones/examples/
            $ python                             # Try 'python -h' to show the script's customizable parameters
            $ cd gym-pybullet-drones/examples/
            $ python                           # Try 'python learn.p  
            copy iconCopy
              author = {Raffin, Antonin},
              title = {RL Baselines3 Zoo},
              year = {2020},
              publisher = {GitHub},
              journal = {GitHub repository},
              howpublished = {\url{}},
            The right way to install stable-baselines?
            Pythondot img4Lines of Code : 2dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from stable_baselines3.sac.policies import MlpPolicy
            ModuleNotFoundError: No module named 'stable_baselines3'
            Pythondot img5Lines of Code : 4dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            !pip install stable-baselines3[extra]
            import stable_baselines3
            What is missing in OpenAI Gym registration?
            Pythondot img6Lines of Code : 8dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
                import gym_basic
            except ImportError:
                gym_basic = None
              # Hyperparameters go here...
            Python - How to get more info about SyntaxError?
            Pythondot img7Lines of Code : 2dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            cat | singularity exec connectx.simg  /usr/bin/python3
            %%writefile magic command in regular Python
            Pythondot img8Lines of Code : 25dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            %%writefile -a
                model = Net()
                model = model.float()
                model ='cpu')
                model = model.eval()
                obs = tensor(obs['board']).reshape(1, 1, config.rows, config.columns).

            Community Discussions


            Rollout summary statistics not being monitored for CustomEnv using Stable-Baselines3
            Asked 2022-Apr-11 at 16:15

            I am trying to train a custom environment using PPO via Stable-Baselines3 and OpenAI Gym. For some reason the rollout statistics are not being reported for this custom environment when I try to train the PPO model.

            The code that I am using is below ( I have not included the code for the CustomEnv for brevity):



            Answered 2022-Apr-11 at 16:15

            SOLVED: There was an edge case where the environment was not ending, and the done variable remained False indefinitely.

            After fixing this bug, the Rollout statistics reappeared.



            What are vectorized environments in reinforcement learning?
            Asked 2022-Mar-25 at 10:37

            I'm having a hard time wrapping my head around what and when vectorized environments should be used. If you can provide an example of a use case, that would be great.

            Documentation of vectorized environments in SB3:



            Answered 2022-Mar-25 at 10:37

            Vectorized Environments are a method for stacking multiple independent environments into a single environment. Instead of executing and training an agent on 1 environment per step, it allows to train the agent on multiple environments per step.

            Usually you also want these environment to have different seeds, in order to gain more diverse experience. This is very useful to speed up training.

            I think they are called "vectorized" since each training step the agent observes multiple states (inserted in a vector), outputs multiple actions (one for each environment), which are inserted in a vector, and receives multiple rewards. Hence the "vectorized" term



            The right way to install stable-baselines?
            Asked 2022-Jan-13 at 12:56

            I am trying to install stable-baselines and run the first two lines from Getting Started section of the online manual but no option is working. I started with



            Answered 2022-Jan-13 at 12:56

            To quote the github readme:

            Note: Stable-Baselines supports Tensorflow versions from 1.8.0 to 1.14.0. Support for Tensorflow 2 API is planned.

            The same github readme also recommends to use stable-baselines3, as stable-baselines is currently only being maintained and its functionality is not extended. Thus, I would not expect the TF1 -> TF2 update any time soon.

            If you can not install this version of tensorflow, I suggest to use stable-baselines3 and follow the examples. The code you posted above is not consistent with the stable-baselines3 docs, which import the MlpPolicy as



            Can't connect controller to specific instance of Webots
            Asked 2021-Sep-09 at 06:35

            I am trying using Webots in combination with stable-baselines3 for reeinforcment-learning purpose. I tried using Webot with multiple instances, but connecting the controller to a specific one doesn't work. It mostly connects to the newest one. I wrote a runtime.ini giving the WEBOT_PID manually looking like



            Answered 2021-Sep-09 at 06:35

            The WEBOTS_PID environment variable should be set before you start the robot controller, otherwise it is not taken into account. So, you cannot set it in the runtime.ini (which is anyhow ignored when launching extern controllers) nor in the controller itself. I would recommend you to write a script that sets the WEBOTS_PID first and then starts the controller program as an extern controller.



            Python - How to get more info about SyntaxError?
            Asked 2020-Nov-12 at 20:45

            I am trying to run Python code on Ubuntu image container with all required libraries.



            Answered 2020-Nov-12 at 20:45


            %%writefile magic command in regular Python
            Asked 2020-Oct-11 at 07:55

            I am copying Python code from Jupyter Notebook to .py file to run it from a terminal. I could not find a way how this line should be implemented:



            Answered 2020-Oct-11 at 07:55

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


            No vulnerabilities reported

            Install stable-baselines3

            Note: Stable-Baselines3 supports PyTorch >= 1.8.1.
            Install the Stable Baselines3 package:. Note: Some shells such as Zsh require quotation marks around brackets, i.e. pip install 'stable-baselines3[extra]' (More Info).
            All unit tests in stable baselines3 can be run using pytest runner:.


            Documentation is available online:
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
          • PyPI

            pip install stable-baselines3

          • CLONE
          • HTTPS


          • CLI

            gh repo clone DLR-RM/stable-baselines3

          • sshUrl


          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link