您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
127 行
3.9 KiB
127 行
3.9 KiB
import pytest
|
|
import attr
|
|
|
|
|
|
from mlagents.trainers.tests.simple_test_envs import (
|
|
SimpleEnvironment,
|
|
MemoryEnvironment,
|
|
RecordEnvironment,
|
|
)
|
|
|
|
from mlagents.trainers.demo_loader import write_demo
|
|
|
|
from mlagents.trainers.settings import (
|
|
NetworkSettings,
|
|
SelfPlaySettings,
|
|
BehavioralCloningSettings,
|
|
GAILSettings,
|
|
RewardSignalType,
|
|
EncoderType,
|
|
FrameworkType,
|
|
)
|
|
|
|
from mlagents_envs.communicator_objects.demonstration_meta_pb2 import (
|
|
DemonstrationMetaProto,
|
|
)
|
|
from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
|
|
from mlagents_envs.communicator_objects.space_type_pb2 import discrete, continuous
|
|
|
|
from mlagents.trainers.tests.dummy_config import ppo_dummy_config, sac_dummy_config
|
|
from mlagents.trainers.tests.check_env_trains import (
|
|
check_environment_trains,
|
|
default_reward_processor,
|
|
)
|
|
|
|
BRAIN_NAME = "1D"
|
|
|
|
PPO_TORCH_CONFIG = attr.evolve(ppo_dummy_config(), framework=FrameworkType.PYTORCH)
|
|
SAC_TORCH_CONFIG = attr.evolve(sac_dummy_config(), framework=FrameworkType.PYTORCH)
|
|
|
|
|
|
def test_hybrid_ppo():
|
|
env = SimpleEnvironment(
|
|
[BRAIN_NAME], continuous_action_size=1, discrete_action_size=1
|
|
)
|
|
config = attr.evolve(PPO_TORCH_CONFIG)
|
|
check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=1.0)
|
|
|
|
|
|
def test_conthybrid_ppo():
|
|
env = SimpleEnvironment(
|
|
[BRAIN_NAME], continuous_action_size=1, discrete_action_size=0
|
|
)
|
|
config = attr.evolve(PPO_TORCH_CONFIG)
|
|
check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=1.0)
|
|
|
|
|
|
def test_dischybrid_ppo():
|
|
env = SimpleEnvironment(
|
|
[BRAIN_NAME], continuous_action_size=0, discrete_action_size=1
|
|
)
|
|
config = attr.evolve(PPO_TORCH_CONFIG)
|
|
check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=1.0)
|
|
|
|
|
|
@pytest.mark.parametrize("num_visual", [1, 2])
|
|
def test_visual_ppo(num_visual):
|
|
env = SimpleEnvironment(
|
|
[BRAIN_NAME],
|
|
num_visual=num_visual,
|
|
num_vector=0,
|
|
continuous_action_size=1,
|
|
discrete_action_size=1,
|
|
)
|
|
new_hyperparams = attr.evolve(
|
|
PPO_TORCH_CONFIG.hyperparameters, learning_rate=3.0e-4
|
|
)
|
|
config = attr.evolve(PPO_TORCH_CONFIG, hyperparameters=new_hyperparams)
|
|
check_environment_trains(env, {BRAIN_NAME: config})
|
|
|
|
|
|
def test_recurrent_ppo():
|
|
env = MemoryEnvironment(
|
|
[BRAIN_NAME], continuous_action_size=1, discrete_action_size=1
|
|
)
|
|
new_network_settings = attr.evolve(
|
|
PPO_TORCH_CONFIG.network_settings,
|
|
memory=NetworkSettings.MemorySettings(memory_size=16),
|
|
)
|
|
new_hyperparams = attr.evolve(
|
|
PPO_TORCH_CONFIG.hyperparameters,
|
|
learning_rate=1.0e-3,
|
|
batch_size=64,
|
|
buffer_size=128,
|
|
)
|
|
config = attr.evolve(
|
|
PPO_TORCH_CONFIG,
|
|
hyperparameters=new_hyperparams,
|
|
network_settings=new_network_settings,
|
|
max_steps=100000,
|
|
)
|
|
check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
|
|
|
|
|
|
# def test_3cdhybrid_ppo():
|
|
# env = SimpleEnvironment(
|
|
# [BRAIN_NAME], continuous_action_size=2, discrete_action_size=1, step_size=0.8
|
|
# )
|
|
# new_hyperparams = attr.evolve(
|
|
# PPO_TORCH_CONFIG.hyperparameters, batch_size=128, buffer_size=1280, beta=0.01
|
|
# )
|
|
# config = attr.evolve(
|
|
# PPO_TORCH_CONFIG, hyperparameters=new_hyperparams, max_steps=10000
|
|
# )
|
|
# check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=1.0)
|
|
#
|
|
#
|
|
# def test_3ddhybrid_ppo():
|
|
# env = SimpleEnvironment(
|
|
# [BRAIN_NAME], continuous_action_size=1, discrete_action_size=2, step_size=0.8
|
|
# )
|
|
# new_hyperparams = attr.evolve(
|
|
# PPO_TORCH_CONFIG.hyperparameters, batch_size=128, buffer_size=1280, beta=0.01
|
|
# )
|
|
# config = attr.evolve(
|
|
# PPO_TORCH_CONFIG, hyperparameters=new_hyperparams, max_steps=10000
|
|
# )
|
|
# check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=1.0)
|