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406662d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | # Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from isaaclab.app import AppLauncher
# launch the simulator
app_launcher = AppLauncher(headless=True, enable_cameras=True)
simulation_app = app_launcher.app
"""Rest everything follows."""
import gymnasium as gym
import pytest
import torch
from tensordict import TensorDict
import carb
import omni.usd
from isaaclab.envs import DirectMARLEnv, multi_agent_to_single_agent
from isaaclab_rl.rsl_rl import RslRlVecEnvWrapper
import isaaclab_tasks # noqa: F401
from isaaclab_tasks.utils.parse_cfg import parse_env_cfg
@pytest.fixture(scope="module")
def registered_tasks():
# acquire all Isaac environments names
registered_tasks = list()
for task_spec in gym.registry.values():
if "Isaac" in task_spec.id:
cfg_entry_point = gym.spec(task_spec.id).kwargs.get("rsl_rl_cfg_entry_point")
if cfg_entry_point is not None:
registered_tasks.append(task_spec.id)
# sort environments by name
registered_tasks.sort()
registered_tasks = registered_tasks[:5]
# this flag is necessary to prevent a bug where the simulation gets stuck randomly when running the
# test on many environments.
carb_settings_iface = carb.settings.get_settings()
carb_settings_iface.set_bool("/physics/cooking/ujitsoCollisionCooking", False)
# print all existing task names
print(">>> All registered environments:", registered_tasks)
return registered_tasks
def test_random_actions(registered_tasks):
"""Run random actions and check environments return valid signals."""
# common parameters
num_envs = 64
device = "cuda"
for task_name in registered_tasks:
# Use pytest's subtests
print(f">>> Running test for environment: {task_name}")
# create a new stage
omni.usd.get_context().new_stage()
# reset the rtx sensors carb setting to False
carb.settings.get_settings().set_bool("/isaaclab/render/rtx_sensors", False)
try:
# parse configuration
env_cfg = parse_env_cfg(task_name, device=device, num_envs=num_envs)
# create environment
env = gym.make(task_name, cfg=env_cfg)
# convert to single-agent instance if required by the RL algorithm
if isinstance(env.unwrapped, DirectMARLEnv):
env = multi_agent_to_single_agent(env)
# wrap environment
env = RslRlVecEnvWrapper(env)
except Exception as e:
if "env" in locals() and hasattr(env, "_is_closed"):
env.close()
else:
if hasattr(e, "obj") and hasattr(e.obj, "_is_closed"):
e.obj.close()
pytest.fail(f"Failed to set-up the environment for task {task_name}. Error: {e}")
# reset environment
obs, extras = env.reset()
# check signal
assert _check_valid_tensor(obs)
assert _check_valid_tensor(extras)
# simulate environment for 100 steps
with torch.inference_mode():
for _ in range(100):
# sample actions from -1 to 1
actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1
# apply actions
transition = env.step(actions)
# check signals
for data in transition:
assert _check_valid_tensor(data), f"Invalid data: {data}"
# close the environment
print(f">>> Closing environment: {task_name}")
env.close()
def test_no_time_outs(registered_tasks):
"""Check that environments with finite horizon do not send time-out signals."""
# common parameters
num_envs = 64
device = "cuda"
for task_name in registered_tasks:
# Use pytest's subtests
print(f">>> Running test for environment: {task_name}")
# create a new stage
omni.usd.get_context().new_stage()
# parse configuration
env_cfg = parse_env_cfg(task_name, device=device, num_envs=num_envs)
# change to finite horizon
env_cfg.is_finite_horizon = True
# create environment
env = gym.make(task_name, cfg=env_cfg)
# wrap environment
env = RslRlVecEnvWrapper(env)
# reset environment
_, extras = env.reset()
# check signal
assert "time_outs" not in extras, "Time-out signal found in finite horizon environment."
# simulate environment for 10 steps
with torch.inference_mode():
for _ in range(10):
# sample actions from -1 to 1
actions = 2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1
# apply actions
extras = env.step(actions)[-1]
# check signals
assert "time_outs" not in extras, "Time-out signal found in finite horizon environment."
# close the environment
print(f">>> Closing environment: {task_name}")
env.close()
"""
Helper functions.
"""
@staticmethod
def _check_valid_tensor(data: torch.Tensor | dict) -> bool:
"""Checks if given data does not have corrupted values.
Args:
data: Data buffer.
Returns:
True if the data is valid.
"""
if isinstance(data, torch.Tensor):
return not torch.any(torch.isnan(data))
elif isinstance(data, TensorDict):
return not data.isnan().any()
elif isinstance(data, dict):
valid_tensor = True
for value in data.values():
if isinstance(value, dict):
valid_tensor &= _check_valid_tensor(value)
elif isinstance(value, torch.Tensor):
valid_tensor &= not torch.any(torch.isnan(value))
return valid_tensor
else:
raise ValueError(f"Input data of invalid type: {type(data)}.")
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