dm_control VS baselines

Compare dm_control vs baselines and see what are their differences.


Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo. (by google-deepmind)


OpenAI Baselines: high-quality implementations of reinforcement learning algorithms (by openai)
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dm_control baselines
7 14
3,520 15,309
2.0% 0.8%
7.5 0.0
5 days ago 5 months ago
Python Python
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.


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.


Posts with mentions or reviews of baselines. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-03.
  • How to proceed further? (Learning RL)
    3 projects | /r/reinforcementlearning | 3 Oct 2022
    Ah sorry I understood your post. It has helped me to code quite a few of them from scratch but you can also check out or similar
  • How to tune hypeparametes in A2C-ppo?
    2 projects | /r/reinforcementlearning | 15 Jun 2022
    Im currently working with A2C. The model was able to learn open ai pong, i ran this as a sanity check that i havent made any bugs. Now im trying to make the model play breakout, but still after 10m steps the model has not made any significant progress. Im using baseline hyperparameters which can be found here, except my buffersize have been from 512 to 4096. Ive noticed that entropy decreases extremely slowly given the buffersize from the interval which i just gave. So my questions are how to make entropy decrease and how to increase rewards per buffer? Ive tried to decrease the entropy coefficient to almost zero, but still it acts very weirdly.
  • Boycotting 2.0 or rather PoS
    2 projects | /r/EtherMining | 15 May 2021
    I used a multitude of agents to train it but the best I found was A3C, there are a bunch of examples here you can use to test their performance (although they may require some tweaking).
  • How to speed up off-policy algorithms?
    2 projects | /r/reinforcementlearning | 21 Apr 2021
    I noticed that off-policy algorithms including DQN, DDPG and TD3 in different baselines and stable-baselines are implemented with a single environment. And even if more environments were added, this won't affect performance because this will only be adding more fresh samples to replay buffer(s). What are some ways to improve speed without major changes to the algorithms? The only thing that I could think of is adding an on-policy update like in ACER but this is going to change the algorithms and I don't know whether it will improve/worsen model convergence.
  • Any beginner resources for RL in Robotics?
    3 projects | /r/robotics | 19 Apr 2021
    OpenAI baselines
  • Convergence of the PPO
    2 projects | /r/reinforcementlearning | 27 Mar 2021
    It might be worth comparing your implementation to the DeepMind PPO1 & 2 ones to see if they have the same side effect:

What are some alternatives?

When comparing dm_control and baselines you can also consider the following projects:

gym - A toolkit for developing and comparing reinforcement learning algorithms.

IsaacGymEnvs - Isaac Gym Reinforcement Learning Environments

pytorch-a2c-ppo-acktr-gail - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

mujoco-py - MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.

Robotics Library (RL) - The Robotics Library (RL) is a self-contained C++ library for rigid body kinematics and dynamics, motion planning, and control.

acme - A library of reinforcement learning components and agents

stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

dreamerv2 - Mastering Atari with Discrete World Models

crafter - Benchmarking the Spectrum of Agent Capabilities

lab - A customisable 3D platform for agent-based AI research

myosuite - MyoSuite is a collection of environments/tasks to be solved by musculoskeletal models simulated with the MuJoCo physics engine and wrapped in the OpenAI gym API.