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Simple and easily configurable grid world environments for reinforcement learning
This package contains implementations of various RL algorithms for continuous control tasks simulated with MuJoCo.
Python package providing easy to use Gym environments and data access for training agents in Minecraft.
Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning
The main features of farama-foundation/metaworld are: Model Evaluation and Benchmarking, Reinforcement Learning Environments, World Models.
Projects with overlapping indexed features include: minerllabs/minerl — Python package providing easy to use Gym environments and data access for training agents in Minecraft. openai/gym — Gym is a reinforcement learning environment toolkit and agent simulation framework. It provides a standardized API and… danijar/crafter — Status: Stable release. maximecb/gym-minigrid — Simple and easily configurable grid world environments for reinforcement learning. aravindr93/mjrl — This package contains implementations of various RL algorithms for continuous control tasks simulated with MuJoCo. openai/mujoco-py — MuJoCo is a physics engine which can do very detailed efficient simulations with contacts. This library lets you use…
