Meta-World is an open-source simulated benchmark for meta-reinforcement learning and multi-task learning consisting of 50 distinct robotic manipulation tasks.
Basic example:
import metaworld
import random
print(metaworld.ML1.ENV_NAMES) # Check out the available environments
ml1 = metaworld.ML1('pick-place-v2') # Construct the benchmark, sampling tasks
env = ml1.train_classes['pick-place-v2']() # Create an environment with task `pick_place`
task = random.choice(ml1.train_tasks)
env.set_task(task) # Set task
obs = env.reset() # Reset environment
a = env.action_space.sample() # Sample an action
obs, reward, terminate, truncate, info = env.step(a)