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[gym_jiminy/common] Speed-up env pipeline. #634

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Aug 18, 2023
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[gym_jiminy/common] Speed-up env pipeline. #634

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18 changes: 14 additions & 4 deletions python/gym_jiminy/common/gym_jiminy/common/envs/env_generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,12 +10,14 @@
from collections.abc import Mapping
from itertools import chain
from typing import (
Dict, Any, List, cast, Optional, Tuple, Callable, Iterable, Union,
SupportsFloat, Iterator, Generic, Sequence, Mapping as MappingT,
Dict, Any, List, cast, no_type_check, Optional, Tuple, Callable, Iterable,
Union, SupportsFloat, Iterator, Generic, Sequence, Mapping as MappingT,
MutableMapping as MutableMappingT)

import tree
import numpy as np
from numpy.core.umath import ( # type: ignore[attr-defined]
copyto as _array_copyto)
from gymnasium import spaces
from gymnasium.core import RenderFrame

Expand Down Expand Up @@ -231,7 +233,6 @@ def __init__(self,
"of custom action spaces.")

# Define specialized operators for efficiency
self._copyto_observation = build_copyto(self.observation)
self._copyto_action = build_copyto(self.action)
self._contains_observation = build_contains(
self.observation, self.observation_space)
Expand Down Expand Up @@ -1376,6 +1377,7 @@ def _refresh_buffers(self) -> None:
`_setup` method.
"""

@no_type_check
def refresh_observation(self, measurement: EngineObsType) -> None:
"""Compute the observation based on the current state of the robot.

Expand All @@ -1396,7 +1398,15 @@ def refresh_observation(self, measurement: EngineObsType) -> None:
checking whether the simulation already started. It is not exactly
the same but it does the job regarding preserving efficiency.
"""
self._copyto_observation(measurement)
observation = self.observation
observation["t"][()] = measurement["t"]
_array_copyto(observation['states']['agent']['q'],
measurement['states']['agent']['q'])
_array_copyto(observation['states']['agent']['v'],
measurement['states']['agent']['v'])
sensors_data = observation['measurements']
for key, value in dict.items(measurement['measurements']):
_array_copyto(sensors_data[key], value)

def compute_command(self, action: ActT) -> np.ndarray:
"""Compute the motors efforts to apply on the robot.
Expand Down