Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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import math
import tempfile
import pytest
import yaml
import numpy as np
from typing import Dict, Any
from mlagents.trainers.tests.simple_test_envs import (
SimpleEnvironment,
MemoryEnvironment,
RecordEnvironment,
)
from mlagents.trainers.trainer_controller import TrainerController
from mlagents.trainers.trainer_util import TrainerFactory
from mlagents.trainers.simple_env_manager import SimpleEnvManager
from mlagents.trainers.sampler_class import SamplerManager
from mlagents.trainers.demo_loader import write_demo
from mlagents.trainers.stats import StatsReporter, StatsWriter, StatsSummary
from mlagents_envs.side_channel.float_properties_channel import FloatPropertiesChannel
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
BRAIN_NAME = "1D"
PPO_CONFIG = f"""
{BRAIN_NAME}:
trainer: ppo
batch_size: 16
beta: 5.0e-3
buffer_size: 64
epsilon: 0.2
hidden_units: 32
lambd: 0.95
learning_rate: 5.0e-3
learning_rate_schedule: constant
max_steps: 3000
memory_size: 16
normalize: false
num_epoch: 3
num_layers: 1
time_horizon: 64
sequence_length: 64
summary_freq: 500
use_recurrent: false
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.99
"""
SAC_CONFIG = f"""
{BRAIN_NAME}:
trainer: sac
batch_size: 8
buffer_size: 500
buffer_init_steps: 100
hidden_units: 16
init_entcoef: 0.01
learning_rate: 5.0e-3
max_steps: 1000
memory_size: 16
normalize: false
num_update: 1
train_interval: 1
num_layers: 1
time_horizon: 64
sequence_length: 32
summary_freq: 100
tau: 0.01
use_recurrent: false
curiosity_enc_size: 128
demo_path: None
vis_encode_type: simple
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.99
"""
def generate_config(
config: str, override_vals: Dict[str, Any] = None
) -> Dict[str, Any]:
trainer_config = yaml.safe_load(config)
if override_vals is not None:
trainer_config[BRAIN_NAME].update(override_vals)
return trainer_config
# The reward processor is passed as an argument to _check_environment_trains.
# It is applied to the list pf all final rewards for each brain individually.
# This is so that we can process all final rewards in different ways for different algorithms.
# Custom reward processors shuld be built within the test function and passed to _check_environment_trains
# Default is average over the last 5 final rewards
def default_reward_processor(rewards, last_n_rewards=5):
rewards_to_use = rewards[-last_n_rewards:]
# For debugging tests
print("Last {} rewards:".format(last_n_rewards), rewards_to_use)
return np.array(rewards[-last_n_rewards:], dtype=np.float32).mean()
class DebugWriter(StatsWriter):
"""
Print to stdout so stats can be viewed in pytest
"""
def __init__(self):
self._last_reward_summary: Dict[str, float] = {}
def get_last_rewards(self):
return self._last_reward_summary
def write_stats(
self, category: str, values: Dict[str, StatsSummary], step: int
) -> None:
for val, stats_summary in values.items():
if val == "Environment/Cumulative Reward":
print(step, val, stats_summary.mean)
self._last_reward_summary[category] = stats_summary.mean
def _check_environment_trains(
env,
trainer_config,
reward_processor=default_reward_processor,
meta_curriculum=None,
success_threshold=0.9,
env_manager=None,
):
# Create controller and begin training.
with tempfile.TemporaryDirectory() as dir:
run_id = "id"
save_freq = 99999
seed = 1337
StatsReporter.writers.clear() # Clear StatsReporters so we don't write to file
debug_writer = DebugWriter()
StatsReporter.add_writer(debug_writer)
if env_manager is None:
env_manager = SimpleEnvManager(env, FloatPropertiesChannel())
trainer_factory = TrainerFactory(
trainer_config=trainer_config,
summaries_dir=dir,
run_id=run_id,
model_path=dir,
keep_checkpoints=1,
train_model=True,
load_model=False,
seed=seed,
meta_curriculum=meta_curriculum,
multi_gpu=False,
)
tc = TrainerController(
trainer_factory=trainer_factory,
summaries_dir=dir,
model_path=dir,
run_id=run_id,
meta_curriculum=meta_curriculum,
train=True,
training_seed=seed,
sampler_manager=SamplerManager(None),
resampling_interval=None,
save_freq=save_freq,
)
# Begin training
tc.start_learning(env_manager)
if (
success_threshold is not None
): # For tests where we are just checking setup and not reward
processed_rewards = [
reward_processor(rewards) for rewards in env.final_rewards.values()
]
assert all(not math.isnan(reward) for reward in processed_rewards)
assert all(reward > success_threshold for reward in processed_rewards)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_simple_ppo(use_discrete):
env = SimpleEnvironment([BRAIN_NAME], use_discrete=use_discrete)
config = generate_config(PPO_CONFIG)
_check_environment_trains(env, config)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_2d_ppo(use_discrete):
env = SimpleEnvironment(
[BRAIN_NAME], use_discrete=use_discrete, action_size=2, step_size=0.5
)
config = generate_config(PPO_CONFIG)
_check_environment_trains(env, config)
@pytest.mark.parametrize("use_discrete", [True, False])
@pytest.mark.parametrize("num_visual", [1, 2])
def test_visual_ppo(num_visual, use_discrete):
env = SimpleEnvironment(
[BRAIN_NAME],
use_discrete=use_discrete,
num_visual=num_visual,
num_vector=0,
step_size=0.2,
)
override_vals = {"learning_rate": 3.0e-4}
config = generate_config(PPO_CONFIG, override_vals)
_check_environment_trains(env, config)
@pytest.mark.parametrize("num_visual", [1, 2])
@pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn"])
def test_visual_advanced_ppo(vis_encode_type, num_visual):
env = SimpleEnvironment(
[BRAIN_NAME],
use_discrete=True,
num_visual=num_visual,
num_vector=0,
step_size=0.5,
vis_obs_size=(36, 36, 3),
)
override_vals = {
"learning_rate": 3.0e-4,
"vis_encode_type": vis_encode_type,
"max_steps": 500,
"summary_freq": 100,
}
config = generate_config(PPO_CONFIG, override_vals)
# The number of steps is pretty small for these encoders
_check_environment_trains(env, config, success_threshold=0.5)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_recurrent_ppo(use_discrete):
env = MemoryEnvironment([BRAIN_NAME], use_discrete=use_discrete)
override_vals = {
"max_steps": 5000,
"batch_size": 64,
"buffer_size": 128,
"learning_rate": 1e-3,
"use_recurrent": True,
}
config = generate_config(PPO_CONFIG, override_vals)
_check_environment_trains(env, config, success_threshold=0.9)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_simple_sac(use_discrete):
env = SimpleEnvironment([BRAIN_NAME], use_discrete=use_discrete)
config = generate_config(SAC_CONFIG)
_check_environment_trains(env, config)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_2d_sac(use_discrete):
env = SimpleEnvironment(
[BRAIN_NAME], use_discrete=use_discrete, action_size=2, step_size=0.8
)
override_vals = {"buffer_init_steps": 2000, "max_steps": 4000}
config = generate_config(SAC_CONFIG, override_vals)
_check_environment_trains(env, config, success_threshold=0.8)
@pytest.mark.parametrize("use_discrete", [True, False])
@pytest.mark.parametrize("num_visual", [1, 2])
def test_visual_sac(num_visual, use_discrete):
env = SimpleEnvironment(
[BRAIN_NAME],
use_discrete=use_discrete,
num_visual=num_visual,
num_vector=0,
step_size=0.2,
)
override_vals = {"batch_size": 16, "learning_rate": 3e-4}
config = generate_config(SAC_CONFIG, override_vals)
_check_environment_trains(env, config)
@pytest.mark.parametrize("num_visual", [1, 2])
@pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn"])
def test_visual_advanced_sac(vis_encode_type, num_visual):
env = SimpleEnvironment(
[BRAIN_NAME],
use_discrete=True,
num_visual=num_visual,
num_vector=0,
step_size=0.5,
vis_obs_size=(36, 36, 3),
)
override_vals = {
"batch_size": 16,
"learning_rate": 3.0e-4,
"vis_encode_type": vis_encode_type,
"buffer_init_steps": 0,
"max_steps": 100,
}
config = generate_config(SAC_CONFIG, override_vals)
# The number of steps is pretty small for these encoders
_check_environment_trains(env, config, success_threshold=0.5)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_recurrent_sac(use_discrete):
env = MemoryEnvironment([BRAIN_NAME], use_discrete=use_discrete)
override_vals = {
"batch_size": 64,
"use_recurrent": True,
"max_steps": 3000,
"learning_rate": 1e-3,
"buffer_init_steps": 500,
}
config = generate_config(SAC_CONFIG, override_vals)
_check_environment_trains(env, config)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_simple_ghost(use_discrete):
env = SimpleEnvironment(
[BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"], use_discrete=use_discrete
)
override_vals = {
"max_steps": 2500,
"self_play": {
"play_against_latest_model_ratio": 1.0,
"save_steps": 2000,
"swap_steps": 2000,
},
}
config = generate_config(PPO_CONFIG, override_vals)
_check_environment_trains(env, config)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_simple_ghost_fails(use_discrete):
env = SimpleEnvironment(
[BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"], use_discrete=use_discrete
)
# This config should fail because the ghosted policy is never swapped with a competent policy.
# Swap occurs after max step is reached.
override_vals = {
"max_steps": 2500,
"self_play": {
"play_against_latest_model_ratio": 1.0,
"save_steps": 2000,
"swap_steps": 4000,
},
}
config = generate_config(PPO_CONFIG, override_vals)
_check_environment_trains(env, config, success_threshold=None)
processed_rewards = [
default_reward_processor(rewards) for rewards in env.final_rewards.values()
]
success_threshold = 0.9
assert any(reward > success_threshold for reward in processed_rewards) and any(
reward < success_threshold for reward in processed_rewards
)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_simple_asymm_ghost(use_discrete):
# Make opponent for asymmetric case
brain_name_opp = BRAIN_NAME + "Opp"
env = SimpleEnvironment(
[BRAIN_NAME + "?team=0", brain_name_opp + "?team=1"], use_discrete=use_discrete
)
override_vals = {
"max_steps": 2000,
"self_play": {
"play_against_latest_model_ratio": 1.0,
"save_steps": 5000,
"swap_steps": 5000,
"team_change": 2000,
},
}
config = generate_config(PPO_CONFIG, override_vals)
config[brain_name_opp] = config[BRAIN_NAME]
_check_environment_trains(env, config)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_simple_asymm_ghost_fails(use_discrete):
# Make opponent for asymmetric case
brain_name_opp = BRAIN_NAME + "Opp"
env = SimpleEnvironment(
[BRAIN_NAME + "?team=0", brain_name_opp + "?team=1"], use_discrete=use_discrete
)
# This config should fail because the team that us not learning when both have reached
# max step should be executing the initial, untrained poliy.
override_vals = {
"max_steps": 2000,
"self_play": {
"play_against_latest_model_ratio": 0.0,
"save_steps": 5000,
"swap_steps": 5000,
"team_change": 2000,
},
}
config = generate_config(PPO_CONFIG, override_vals)
config[brain_name_opp] = config[BRAIN_NAME]
_check_environment_trains(env, config, success_threshold=None)
processed_rewards = [
default_reward_processor(rewards) for rewards in env.final_rewards.values()
]
success_threshold = 0.99
assert any(reward > success_threshold for reward in processed_rewards) and any(
reward < success_threshold for reward in processed_rewards
)
@pytest.fixture(scope="session")
def simple_record(tmpdir_factory):
def record_demo(use_discrete, num_visual=0, num_vector=1):
env = RecordEnvironment(
[BRAIN_NAME],
use_discrete=use_discrete,
num_visual=num_visual,
num_vector=num_vector,
n_demos=100,
)
# If we want to use true demos, we can solve the env in the usual way
# Otherwise, we can just call solve to execute the optimal policy
env.solve()
agent_info_protos = env.demonstration_protos[BRAIN_NAME]
meta_data_proto = DemonstrationMetaProto()
brain_param_proto = BrainParametersProto(
vector_action_size=[1],
vector_action_descriptions=[""],
vector_action_space_type=discrete if use_discrete else continuous,
brain_name=BRAIN_NAME,
is_training=True,
)
action_type = "Discrete" if use_discrete else "Continuous"
demo_path_name = "1DTest" + action_type + ".demo"
demo_path = str(tmpdir_factory.mktemp("tmp_demo").join(demo_path_name))
write_demo(demo_path, meta_data_proto, brain_param_proto, agent_info_protos)
return demo_path
return record_demo
@pytest.mark.parametrize("use_discrete", [True, False])
@pytest.mark.parametrize("trainer_config", [PPO_CONFIG, SAC_CONFIG])
def test_gail(simple_record, use_discrete, trainer_config):
demo_path = simple_record(use_discrete)
env = SimpleEnvironment([BRAIN_NAME], use_discrete=use_discrete, step_size=0.2)
override_vals = {
"max_steps": 500,
"behavioral_cloning": {"demo_path": demo_path, "strength": 1.0, "steps": 1000},
"reward_signals": {
"gail": {
"strength": 1.0,
"gamma": 0.99,
"encoding_size": 32,
"demo_path": demo_path,
}
},
}
config = generate_config(trainer_config, override_vals)
_check_environment_trains(env, config, success_threshold=0.9)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_gail_visual_ppo(simple_record, use_discrete):
demo_path = simple_record(use_discrete, num_visual=1, num_vector=0)
env = SimpleEnvironment(
[BRAIN_NAME],
num_visual=1,
num_vector=0,
use_discrete=use_discrete,
step_size=0.2,
)
override_vals = {
"max_steps": 1000,
"learning_rate": 3.0e-4,
"behavioral_cloning": {"demo_path": demo_path, "strength": 1.0, "steps": 1000},
"reward_signals": {
"gail": {
"strength": 1.0,
"gamma": 0.99,
"encoding_size": 32,
"demo_path": demo_path,
}
},
}
config = generate_config(PPO_CONFIG, override_vals)
_check_environment_trains(env, config, success_threshold=0.9)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_gail_visual_sac(simple_record, use_discrete):
demo_path = simple_record(use_discrete, num_visual=1, num_vector=0)
env = SimpleEnvironment(
[BRAIN_NAME],
num_visual=1,
num_vector=0,
use_discrete=use_discrete,
step_size=0.2,
)
override_vals = {
"max_steps": 500,
"batch_size": 16,
"learning_rate": 3.0e-4,
"behavioral_cloning": {"demo_path": demo_path, "strength": 1.0, "steps": 1000},
"reward_signals": {
"gail": {
"strength": 1.0,
"gamma": 0.99,
"encoding_size": 32,
"demo_path": demo_path,
}
},
}
config = generate_config(SAC_CONFIG, override_vals)
_check_environment_trains(env, config, success_threshold=0.9)