Expert Trajectories

Expert Policies

For each individual environment in Meta-World (i.e. reach-v3, basketball-v3, sweep-v3) there are expert policies that solve the task. These policies can be used to generate expert data for imitation learning tasks.

Using Expert Policies

The below example provides sample code for the reach environment. This code can be extended to the ML10/ML45/MT10/MT50 sets if a list of policies is maintained.

import gymnasium as gym
import metaworld
from metaworld.policies.sawyer_reach_v3_policy import SawyerReachV3Policy

env = gym.make('Meta-World/MT1', env_name='reach-v3')

obs, info = env.reset()

policy = SawyerReachV3Policy()

done = False

while not done:
    a = policy.get_action(obs)
    obs, _, _, _, info = env.step(a)
    done = int(info['success']) == 1