mkurman -----|---------|------|
| 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
- Use
MT10/MT50for multi-task RL andML10/ML45for meta-RL. - Task IDs in MT benchmarks matter — exploit them in your policy architecture.
- Meta-learning benchmarks require separate train/test envs by design.
- Async vectorization improves throughput but increases process overhead.
- Custom benchmark subsets are valuable for targeted ablations.