metaworld

name	metaworld
description	Robotics multi-task and meta-reinforcement learning benchmark (Meta-World). Standardized continuous-control benchmark built on Gymnasium with MT1, MT10, MT50 multi-task suites and ML1, ML10, ML45 meta-learning suites. Supports synchronous and asynchronous vector execution. Use for robotic manipulation benchmarking, multi-task RL, meta-RL adaptation, and evaluating generalization across tasks and goals.
license	MIT license
tags	["robotic-manipulation","multi-task-rl","meta-rl","continuous-control","metaworld"]
metadata	{"skill-author":"K-Dense Inc."}

-----|---------|------| | MT1 | Multi-task learning on one selected task family | 1 task | | MT10 | Multi-task learning across 10 manipulation tasks | 10 tasks | | MT50 | Broad multi-task benchmark | 50 tasks | | ML1-train/test | Meta-learning goal variation within one task family | 1 family | | ML10-train/test | Meta-learning across train/test task split | 10 train + 5 test | | ML45-train/test | Large-scale meta-learning split | 45 train + 5 test |

4. Multi-Task Benchmarks

MT1:

import gymnasium as gym
import metaworld

env = gym.make("Meta-World/MT1", env_name="reach-v3", seed=42)
obs, info = env.reset()
action = env.action_space.sample()
obs, reward, terminated, truncated, info = env.step(action)

MT10 synchronous vectorized:

import gymnasium as gym
import metaworld

envs = gym.make_vec("Meta-World/MT10", vector_strategy="sync", seed=42)
obs, info = envs.reset()
actions = envs.action_space.sample()
obs, rewards, terminations, truncations, infos = envs.step(actions)

MT10 asynchronous vectorized:

envs = gym.make_vec("Meta-World/MT10", vector_strategy="async", seed=42)

MT50:

envs = gym.make_vec("Meta-World/MT50", vector_strategy="sync", seed=42)

5. Meta-Learning Benchmarks

ML1:

import gymnasium as gym
import metaworld

train_env = gym.make("Meta-World/ML1-train", env_name="reach-v3", seed=42)
test_env = gym.make("Meta-World/ML1-test", env_name="reach-v3", seed=42)

ML10 / ML45:

train_envs = gym.make_vec("Meta-World/ML10-train", vector_strategy="sync", seed=42)
test_envs = gym.make_vec("Meta-World/ML10-test", vector_strategy="sync", seed=42)

train_envs = gym.make_vec("Meta-World/ML45-train", vector_strategy="async", seed=42)
test_envs = gym.make_vec("Meta-World/ML45-test", vector_strategy="async", seed=42)

6. Custom Benchmarks

Build your own custom multi-task or meta-learning benchmark:

import gymnasium as gym
import metaworld

envs = gym.make_vec(
    "Meta-World/custom-mt-envs",
    vector_strategy="sync",
    envs_list=["reach-v3", "push-v3", "drawer-open-v3"],
    seed=42,
)

meta_envs = gym.make_vec(
    "Meta-World/custom-ml-envs",
    vector_strategy="async",
    envs_list=["reach-v3", "push-v3", "window-open-v3"],
    seed=42,
)

7. Observation Semantics

• Multi-task environments append one-hot task IDs for task-conditioned policies.
• Meta-learning environments are partially observable to force adaptation.
• Action spaces are continuous control, suitable for PPO/SAC/TD3-style algorithms.

8. Typical Training Patterns

Single-task SAC / PPO:

env = gym.make("Meta-World/MT1", env_name="drawer-open-v3")
# Train with Stable-Baselines3 SAC/PPO or CleanRL continuous-control PPO

Task-conditioned multi-task policy:

envs = gym.make_vec("Meta-World/MT10", vector_strategy="sync")
# Use policy network with task ID appended to observation
# Shared backbone + task-conditioned policy/value heads is common

Meta-RL loop:

# Train on ML10-train, evaluate fast adaptation on ML10-test
# Measure reward after K adaptation episodes/gradient steps

9. Evaluation Recommendations

• Report mean success rate and mean return, not only reward.
• Separate train-task and held-out test-task performance for meta-RL.
• Fix seeds and benchmark version for comparability.
• Use sync mode for lower resource usage; async for more throughput.
• Document task subsets if using custom benchmarks.

10. Integration Notes

• API follows Gymnasium exactly.
• Works naturally with SB3 continuous-control algorithms and custom PyTorch loops.
• Often paired with SAC, PPO, PEARL, MAML-style meta-RL, and task-conditioned transformers.

Key Patterns

1. Use MT10/MT50 for multi-task RL and ML10/ML45 for meta-RL.
2. Task IDs in MT benchmarks matter — exploit them in your policy architecture.
3. Meta-learning benchmarks require separate train/test envs by design.
4. Async vectorization improves throughput but increases process overhead.
5. Custom benchmark subsets are valuable for targeted ablations.

References

• Meta-World Documentation
• Benchmark paper / Meta-World+
• Farama Meta-World repository