garage VS metaworld

Compare garage vs metaworld and see what are their differences.

garage

A toolkit for reproducible reinforcement learning research. (by rlworkgroup)

metaworld

Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning [Moved to: https://github.com/Farama-Foundation/Metaworld] (by rlworkgroup)
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garage metaworld
5 2
1,813 829
0.4% -
0.0 3.5
almost 1 year ago over 1 year ago
Python Python
MIT License 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.
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.

garage

Posts with mentions or reviews of garage. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-22.

metaworld

Posts with mentions or reviews of metaworld. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-22.
  • Are there any follow-up studies of RL^2 algorithms?
    2 projects | /r/reinforcementlearning | 22 Jan 2022
    Hi r/reinforcementlearning! I recently started to be interested in meta-reinforcement learning, and I am particularly interested in models using recurrent neural networks such as RL2. But after few search I found that most of the recent approach for meta-reinforcement learning is based on MARL method, Although RL2 performed very well in meta rl benchmark paper, meta-world. And it was hard to find follow-up research of RL2 at the same time. Does anyone knows about follow-up researches of RL2?
  • [D] Creating benchmarks for reinforcement learning
    2 projects | /r/MachineLearning | 24 May 2021
    How long does it take to write a benchmark for RL like meta-world (https://github.com/rlworkgroup/metaworld) or multiagent emergence environments (https://github.com/openai/multi-agent-emergence-environments)?

What are some alternatives?

When comparing garage and metaworld you can also consider the following projects:

lightning-hydra-template - PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡

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).

Metaworld - Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning

tianshou - An elegant PyTorch deep reinforcement learning library.

VMAgent - Our VMAgent is a platform for exploiting Reinforcement Learning (RL) on Virtual Machine (VM) scheduling tasks.

multi-agent-emergence-environments - Environment generation code for the paper "Emergent Tool Use From Multi-Agent Autocurricula"

nn-template - Generic template to bootstrap your PyTorch project.

rl_lib - Series of deep reinforcement learning algorithms 🤖

agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.