reinforcement-learning-an-introduction
Python Implementation of Reinforcement Learning: An Introduction (by ShangtongZhang)
dm_control
Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo. (by google-deepmind)
reinforcement-learning-an-introduction | dm_control | |
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2 | 7 | |
13,229 | 3,578 | |
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2.7 | 7.5 | |
about 1 month ago | 3 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
reinforcement-learning-an-introduction
Posts with mentions or reviews of reinforcement-learning-an-introduction.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-09.
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Help request: Are the results of Sutton and Barto's Example 6.6 Cliff walking believable? What's likely the problem if my SARSA implementation can't replicate?
The python code to generate any figure in this textbook is reproduced in a repo, and you can find the file for the figure in question here: https://github.com/ShangtongZhang/reinforcement-learning-an-introduction/blob/master/chapter06/cliff_walking.py
- Reinforcement Learning - looking for some resources
dm_control
Posts with mentions or reviews of dm_control.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-25.
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:
Have you ever wanted to use dm-control with stable-baselines3? Within Reinforcement learning (RL), a number of APIs are used to implement environments, with limited ability to convert between them. This makes training agents across different APIs highly difficult, and has resulted in a fractured ecosystem.
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Installing & Using MuJoCo 2.1.5 with OpenAi Gym
Deepmind Control Suite is a good alternative to Open AI Gym for continuous control tasks. It contains many of the environments present in Gym and also a few extra ones. Deepmind Control Suite also uses Mujoco. I found the installation to be straightforward. Check out https://github.com/deepmind/dm_control
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Is there a way to get PPO controlled agents to move a little more gracefully?
Do you know if this is implemented in code anywhere? I've been digging around in DeepMind's dm_control for the past few hours and I haven't found it. I'm not sure what I'm looking for either.
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[D] MuJoCo vs PyBullet? (esp. for custom environment)
If you're interested in using Mujoco, I'd suggest checking out the dm_control package for Python bindings rather than interfacing with C++ directly. I think one downside to Mujoco currently is that you cannot dynamically add objects, and the entire simulation is initialized and loaded according to the MJCF / XML file.
- How to use MuJoCo from Python3
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Any beginner resources for RL in Robotics?
DeepMind's dm control: https://github.com/deepmind/dm_control