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D4RL is an open-source benchmark for offline reinforcement learning. It provides standardized environments and datasets for training and benchmarking algorithms. A supplementary whitepaper and website are also available.
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.
This package contains implementations of various RL algorithms for continuous control tasks simulated with MuJoCo.
MuJoCo is a physics engine which can do very detailed efficient simulations with contacts. This library lets you use MuJoCo from Python.
The main features of openai/mujoco-py are: Model-Based Reinforcement Learning, Reinforcement Learning Environments, World Models, Environment Benchmarks.
Open-source alternatives to openai/mujoco-py include: rail-berkeley/d4rl — D4RL is an open-source benchmark for offline reinforcement learning. It provides standardized environments and… openai/gym — Gym is a reinforcement learning environment toolkit and agent simulation framework. It provides a standardized API and… maximecb/gym-minigrid — Simple and easily configurable grid world environments for reinforcement learning. deepmind/lab — Lab is a customizable 3D platform and research testbed designed for training and testing autonomous agents using… aravindr93/mjrl — This package contains implementations of various RL algorithms for continuous control tasks simulated with MuJoCo. danijar/crafter — Status: Stable release.
