dm_control
crafter
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| dm_control | crafter | |
|---|---|---|
| 7 | 2 | |
| 3,540 | 348 | |
| 2.5% | - | |
| 7.5 | 5.4 | |
| 4 days ago | 3 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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dm_control
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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
crafter
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[R] Benchmarking the Spectrum of Agent Capabilities: They introduce Crafter, an open world survival game with visual inputs that evaluates a wide range of general abilities within a single environment.
Repo for Crafter: https://github.com/danijar/crafter
- Crafter - Open world survival environment for reinforcement learning.
What are some alternatives?
gym - A toolkit for developing and comparing reinforcement learning algorithms.
ma-gym - A collection of multi agent environments based on OpenAI gym.
baselines - OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
FoliClientInstaller - Installs the Foli Client Minecraft modpack
IsaacGymEnvs - Isaac Gym Reinforcement Learning Environments
ITAQA-air-quality-aggregator - Framework to collect, aggregate and process air pollution data from different sources (Italy regions)
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).
portfolio-management - Repository for portfolio management using Pytorch, SQLAlchemy and XArray. The management is done using the reinforcement learning algorithm "Soft Actor-Critic".
mujoco-py - MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.
dreamerv2 - Mastering Atari with Discrete World Models
Robotics Library (RL) - The Robotics Library (RL) is a self-contained C++ library for rigid body kinematics and dynamics, motion planning, and control.
ecobalyse - Accélerer la mise en place de l'affichage environnemental