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111 行
2.9 KiB
111 行
2.9 KiB
from typing import List, Tuple
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from mlagents_envs.base_env import ObservationSpec, DimensionProperty, ObservationType
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import pytest
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import copy
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import os
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from mlagents.trainers.settings import (
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POCASettings,
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TrainerSettings,
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PPOSettings,
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SACSettings,
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GAILSettings,
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CuriositySettings,
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RewardSignalSettings,
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NetworkSettings,
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TrainerType,
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RewardSignalType,
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ScheduleType,
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)
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CONTINUOUS_DEMO_PATH = os.path.dirname(os.path.abspath(__file__)) + "/test.demo"
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DISCRETE_DEMO_PATH = os.path.dirname(os.path.abspath(__file__)) + "/testdcvis.demo"
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_PPO_CONFIG = TrainerSettings(
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trainer_type=TrainerType.PPO,
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hyperparameters=PPOSettings(
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learning_rate=5.0e-3,
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learning_rate_schedule=ScheduleType.CONSTANT,
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batch_size=16,
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buffer_size=64,
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),
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network_settings=NetworkSettings(num_layers=1, hidden_units=32),
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summary_freq=500,
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max_steps=3000,
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threaded=False,
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)
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_SAC_CONFIG = TrainerSettings(
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trainer_type=TrainerType.SAC,
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hyperparameters=SACSettings(
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learning_rate=5.0e-3,
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learning_rate_schedule=ScheduleType.CONSTANT,
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batch_size=8,
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buffer_init_steps=100,
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buffer_size=5000,
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tau=0.01,
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init_entcoef=0.01,
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),
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network_settings=NetworkSettings(num_layers=1, hidden_units=16),
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summary_freq=100,
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max_steps=1000,
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threaded=False,
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)
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_POCA_CONFIG = TrainerSettings(
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trainer_type=TrainerType.POCA,
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hyperparameters=POCASettings(
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learning_rate=5.0e-3,
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learning_rate_schedule=ScheduleType.CONSTANT,
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batch_size=16,
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buffer_size=64,
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),
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network_settings=NetworkSettings(num_layers=1, hidden_units=32),
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summary_freq=500,
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max_steps=3000,
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threaded=False,
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)
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def ppo_dummy_config():
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return copy.deepcopy(_PPO_CONFIG)
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def sac_dummy_config():
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return copy.deepcopy(_SAC_CONFIG)
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def poca_dummy_config():
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return copy.deepcopy(_POCA_CONFIG)
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@pytest.fixture
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def gail_dummy_config():
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return {RewardSignalType.GAIL: GAILSettings(demo_path=CONTINUOUS_DEMO_PATH)}
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@pytest.fixture
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def curiosity_dummy_config():
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return {RewardSignalType.CURIOSITY: CuriositySettings()}
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@pytest.fixture
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def extrinsic_dummy_config():
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return {RewardSignalType.EXTRINSIC: RewardSignalSettings()}
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def create_observation_specs_with_shapes(
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shapes: List[Tuple[int, ...]]
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) -> List[ObservationSpec]:
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obs_specs: List[ObservationSpec] = []
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for i, shape in enumerate(shapes):
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dim_prop = (DimensionProperty.UNSPECIFIED,) * len(shape)
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if len(shape) == 2:
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dim_prop = (DimensionProperty.VARIABLE_SIZE, DimensionProperty.NONE)
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spec = ObservationSpec(
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name=f"observation {i} with shape {shape}",
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shape=shape,
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dimension_property=dim_prop,
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observation_type=ObservationType.DEFAULT,
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)
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obs_specs.append(spec)
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return obs_specs
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