Arcade-Learning-Environment
dm_control
| Arcade-Learning-Environment | dm_control | |
|---|---|---|
| 6 | 7 | |
| 2,080 | 3,569 | |
| 0.7% | 2.2% | |
| 5.3 | 7.5 | |
| 5 days ago | 6 days ago | |
| C++ | Python | |
| GNU General Public License v3.0 only | Apache License 2.0 |
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Arcade-Learning-Environment
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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:
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How to apply Deep RL in Arcade Learning Environment?
They are talking about this: https://github.com/mgbellemare/Arcade-Learning-Environment
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How are rewards/scores calculated in openai Gym's Atari Skiing-v0?
The code for Atari envs is not on gym, but on ALE
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Merge Dragon Bot
If you're more interested in playing games directly from pixel-level input, check out the Arcade Learning Environment, which lets you do this with all the old Atari games. You can find lots of tutorials online about using "reinforcement learning" to play these games.
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[News] The Arcade Learning Environment: Version 0.7
I glanced over everything in this post, for a more detailed explainer check out the following blog post: https://brosa.ca/blog/ale-release-v0.7 and the release notes at https://github.com/mgbellemare/Arcade-Learning-Environment/releases/tag/v0.7.0.
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ROM differences in Atari gym
I'm running some experiments on Atari via gym and have noticed that the MD5 checksums on around half of the ROMs supplied by gym[atari] differ from the MD5s listed here. Has anyone noticed this before, and would it make a difference to the results?
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
What are some alternatives?
Shimmy - An API conversion tool for popular external reinforcement learning environments
gym - A toolkit for developing and comparing reinforcement learning algorithms.
lab - A customisable 3D platform for agent-based AI research
baselines - OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
PettingZoo - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
IsaacGymEnvs - Isaac Gym Reinforcement Learning Environments
meltingpot - A suite of test scenarios for multi-agent reinforcement learning.
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
dreamerv2 - Mastering Atari with Discrete World Models