Projects sharing features with Robodesk

V-D4RL provides pixel-based analogues of the popular D4RL benchmarking tasks, derived from the dmcontrol suite, along with natural extensions of two state-of-the-art online pixel-based continuous control algorithms, DrQ-v2 and DreamerV2, to the offline setting. For further details, please see…

Status: Stable release

Deep Hierarchical Planning from Pixels

MiniHack is a sandbox framework for easily designing rich and diverse environments for Reinforcement Learning (RL). Based on the game of NetHack, MiniHack uses the NetHack Learning Environment (NLE) to communicate with the game and to provide a convenient interface for customly created RL…

A collection of reference environments for offline reinforcement learning

A collection of environments for autonomous driving and tactical decision-making tasks, developed and maintained by Edouard Leurent.

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

Simple and easily configurable grid world environments for reinforcement learning

bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent with two main objectives.

This repo has moved to https://github.com/haosulab/ManiSkill. This repo now serves to ensure the old documentation link https://haosulab.github.io/ManiSkill2/ points to the new documentation

Status: Stable release

Simple and easily configurable grid world environments for reinforcement learning

This is the code accompanying the paper Deep Variational Reinforcement Learning for POMDPs by Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood and Shimon Whiteson.

CALVIN - A benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks

Python package providing easy to use Gym environments and data access for training agents in Minecraft.

Sokoban is Japanese for warehouse keeper and a traditional video game. The game is a transportation puzzle, where the player has to push all boxes in the room on the storage locations/ targets. The possibility of making irreversible mistakes makes these puzzles so challenging especially for…

Authors :: Vikash Kumar (vikashplus@gmail.com), Vittorio Caggiano (caggiano@gmail.com) ================================================= -->

Myriad is a real-world testbed that aims to bridge the gap between trajectory optimization and deep learning. Myriad offers many real-world relevant, continuous-time dynamical system environments, and several trajectory optimization algorithms. These are all written in JAX, and as such can be…

Gym is a reinforcement learning environment toolkit and agent simulation framework. It provides a standardized API and a universal communication interface that defines how learning agents interact with simulation environments through actions and observations. The project includes a benchmark environment suite and a diverse library of pre-configured simulation worlds, including physics engines and classic control tasks. It enables the creation of custom simulation environments to train agents in specific operational scenarios while ensuring reproducibility across different learning algorithms.

MuJoCo is a physics engine which can do very detailed efficient simulations with contacts. This library lets you use MuJoCo from Python.

GoBigger Doc (中文版)

SMAC is WhiRL's environment for research in the field of cooperative multi-agent reinforcement learning (MARL) based on Blizzard's StarCraft II RTS game. SMAC makes use of Blizzard's StarCraft II Machine Learning API and DeepMind's PySC2 to provide a convenient interface for autonomous agents to…

This repository is the interface for the offline reinforcement learning benchmark NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning.