Simple and easily configurable 3D FPS-game-like environments for reinforcement learning
MiniWorld is a minimalistic 3D interior environment simulator for reinforcement learning & robotics research that allows environments to be easily edited like Minigrid meets DM Lab. It can simulate environments with rooms, doors, hallways, and various objects (e.g., office and home environments, mazes). Miniworld 2.0.0 is the first mature release within Farama. This version transitions from gym to gymnasium. Additionally, this release adds CI testing, code standardization pipeline, tests for environments, and documentation for each environment.
Furthermore, we have a website (https://miniworld.farama.org/) that has documentation that covers all the relevant details to start implementing a reinforcement learning agent. In future releases, we plan to add tutorials and more detailed documentation.
Gymnasium v0.26.2
by @BolunDai0216 in https://github.com/Farama-Foundation/Miniworld/pull/72
EzPickle
inheritance to the environments which enables pickling and unpickling objects via their constructor arguments by @BolunDai0216 in https://github.com/Farama-Foundation/Miniworld/pull/76
gym-miniworld
work without updating NVIDIA drivers by @ptigas in https://github.com/Farama-Foundation/Miniworld/pull/40
manual_control.py
by @BolunDai0216 in https://github.com/Farama-Foundation/Miniworld/pull/73
manual_control.py
by @BolunDai0216 in https://github.com/Farama-Foundation/Miniworld/pull/77