Farama-Foundation/Metaworld

What it solves

Meta-World is designed to provide a standardized, open-source benchmark for developing and evaluating reinforcement learning (RL) algorithms. Specifically, it focuses on continuous control robotic manipulation tasks, ensuring that researchers can consistently measure progress in multi-task and meta-reinforcement learning.

How it works

The project provides a suite of 50 different robotic manipulation environments. It integrates with the Gymnasium API, allowing users to create benchmarks based on these tasks. It offers two primary types of benchmarks:

• Multi-Task Benchmarks (MT1, MT10, MT50): These allow a single policy to learn multiple tasks simultaneously. In larger sets (MT10, MT50), task IDs are appended to the state observations.
• Meta-Learning Benchmarks (ML1, ML10, ML45): These focus on few-shot adaptation, testing how well an agent can adapt to goal variations within a single task or adapt to entirely new tasks.

Environments can be run in either synchronous (single process) or asynchronous (isolated processes) modes to optimize for available compute resources.

Who it’s for

It is intended for researchers and developers working on reinforcement learning, specifically those focusing on robotic manipulation, multi-task learning, and meta-learning.

Highlights

• Gymnasium API Compatibility: Uses a familiar interface for environment creation and interaction.
• Diverse Task Set: Includes 50 distinct robotic manipulation tasks.
• ** uma own custom benchmarks:** Users can combine any number of Meta-World environments to create their own custom multi-task or meta-learning benchmarks.
• Flexible Execution: Supports both synchronous and asynchronous vector environments for efficient training.