open_spiel VS brax

Compare open_spiel vs brax and see what are their differences.

open_spiel

OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. (by google-deepmind)

brax

Massively parallel rigidbody physics simulation on accelerator hardware. (by google)
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open_spiel brax
44 11
3,969 2,021
1.4% 3.0%
9.4 5.2
3 days ago 15 days ago
C++ Jupyter Notebook
Apache License 2.0 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.
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.

open_spiel

Posts with mentions or reviews of open_spiel. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-26.

brax

Posts with mentions or reviews of brax. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-09-11.
  • Brax vs TDS for differentiable rigid body dynamics
    2 projects | /r/robotics | 11 Sep 2022
    I need differentiable rigid body dynamics because I want to do nonlinear MPC. One library that can do this is C++ is Tiny Differentiable Simulator https://github.com/erwincoumans/tiny-differentiable-simulator. As I understand it, this software uses a C++ auto-diff library and code generation to create CUDA kernels to compute fast derivatives in parallel. This seems pretty fast because it's C++. Another option is Brax https://github.com/google/brax. Brax uses JAX which I've never used, but from what I've seen online, JAX is popular for researchers and probably very good.
  • Deep learning for robotics
    3 projects | /r/buildapc | 4 Apr 2022
    I am doing a MSc on robotics with a focus on machine learning, especially attention based architectures. There is a lot simulation and reinforcement learning going on. I have a funding of ~2500$ for the hardware system (no flexibility here, cannot use it for cloud either). I used pcpartpicker.com to select compatible components, as shown below. I am not located in the western part of the world; which means I have difficulty accessing some components and prices are higher here than that of pcpartpicker.com. That is why I am aiming towards 2000 - 2200$ range in the pcpartpicker.com. - Overall, what do you think of my planned setup? - Since there is a lot of simulation planned including rigid body dynamics with contact (libraries like https://github.com/raisimTech/raisimLib, https://github.com/deepmind/mujoco), I need some powerful CPU to use these libraries. I know that Intel has MKL over AMD; however, I am not sure how relevant that is for my case. The robotics simulators are generally written with C++, uses Eigen or their own math libraries. I feel like there is a lot of linear algebra involved and Intel combined with MKL should give me less headache. I have chosen i9-12900K, but what about AMD Ryzen9 5950X for example? - There is a new generation of rigid body simulators which use GPU instead of CPU (https://github.com/google/brax, https://developer.nvidia.com/isaac-gym). I do not think they are as mature as the previously mentioned simulators. Perhaps I am mistaken. Shall I focus on them instead? In terms of hardware that means I can downgrade the CPU to Ryzen5, and upgrade to RTX3080, roughly. - Do you think this system is easy to upgrade in future? What can I change to make it easier for long-term use and upgrades? Thanks for any help!
  • [D] Advice on Hardware Setup for Robotics
    3 projects | /r/MachineLearning | 4 Apr 2022
    There is a new generation of rigid body simulators which use GPU instead of CPU (https://github.com/google/brax, https://developer.nvidia.com/isaac-gym). I do not think they are as mature as the previously mentioned simulators. Perhaps I am mistaken. Shall I focus on them instead? In terms of hardware that means I can downgrade the CPU to Ryzen5, and upgrade to RTX3080, roughly.
  • DeepMind open-sourcing MuJoCo simulator
    3 projects | news.ycombinator.com | 18 Oct 2021
    I wonder what this means for the future of Brax [1].

    1. https://github.com/google/brax

  • Any tutorial on how to create RL C++ environments?
    7 projects | /r/reinforcementlearning | 5 Oct 2021
    If you want raw speed, parallel execution on GPU or TPU is best. Checkout out our Brax simulator, which uses the XLA compiler and JAX Python frontend: https://github.com/google/brax
  • [N] Mujoco is free for everyone until October 31 2021
    2 projects | /r/MachineLearning | 28 Jul 2021
    Anyone made any progress with Brax? That was sold as a massively-parallel Mujoco alternative but not sure if anyone's actually using it yet.
  • [R] Brax: A Differentiable Physics Engine for Large Scale Rigid Body Simulation, with a focus on performance and parallelism on accelerators, written in JAX.
    2 projects | /r/MachineLearning | 30 Jun 2021

What are some alternatives?

When comparing open_spiel and brax you can also consider the following projects:

mujoco - Multi-Joint dynamics with Contact. A general purpose physics simulator.

pybullet-gym - Open-source implementations of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform.

muzero-general - MuZero

PettingZoo - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities

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

tiny-differentiable-simulator - Tiny Differentiable Simulator is a header-only C++ and CUDA physics library for reinforcement learning and robotics with zero dependencies.

rlcard - Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.

jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

gym-battleship - Battleship environment for reinforcement learning tasks

TexasHoldemSolverJava - A Java implemented Texas holdem and short deck Solver

RustyNEAT - Rust implementation of NEAT algorithm (HyperNEAT + ES-HyperNEAT + NoveltySearch + CTRNN + L-systems)

tensortrade - An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.