VGCP_robosuite / configs /constants.py
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from typing import Dict
from ml_collections import FrozenConfigDict
# The tasks we in MetaWorld.
METAWORLDTASKS = frozenset([
"assembly",
"basketball",
"button-press",
"button-press-topdown",
"door-close",
"door-open",
"drawer-open",
"faucet-close",
"faucet-open",
"hammer",
"handle-press",
"push",
"shelf-place",
])
# The tasks in robosuite
ROBOSUITETASKS = frozenset([
"NutAssemblySquare",
"Stack",
"Lift",
])
# A mapping from MetaWorld task to Gym environment name.
METAWORLD_TASK_TO_ENV_NAME: Dict[str, str] = {
k: f"{k}-v2-goal-observable"
for k in METAWORLDTASKS
}
# All available encoders.
ALGORITHMS = frozenset([
"xirl",
"tcn",
"lifs",
"goal_classifier",
])
# A mapping from x-MAGICAL embodiment to RL training iterations.
MetaWorldTrainingIterations = FrozenConfigDict({
"assembly": 2_000_000,
"basketball": 3_000_000,
"button-press": 10_000_000,
"button-press-topdown": 10_000_000,
"door-close": 2_400_000,
"door-open": 1_000_000,
"drawer-open": 1_000_000,
"faucet-close": 10_000_000,
"faucet-open": 1_000_000,
"hammer": 1_500_000,
"handle-press": 10_000_000,
"push": 10_000_000,
"shelf-place": 10_000_000,
})
RobosuiteTrainingIterations = FrozenConfigDict({
"NutAssemblySquare": 2_000_000,
"Stack": 2_000_000,
"Lift": 2_000_000,
})