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
99 行
3.6 KiB
99 行
3.6 KiB
import math
|
|
import tempfile
|
|
import numpy as np
|
|
from typing import Dict
|
|
from mlagents.trainers.trainer_controller import TrainerController
|
|
from mlagents.trainers.trainer import TrainerFactory
|
|
from mlagents.trainers.simple_env_manager import SimpleEnvManager
|
|
from mlagents.trainers.stats import StatsReporter, StatsWriter, StatsSummary
|
|
from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
|
|
from mlagents_envs.side_channel.environment_parameters_channel import (
|
|
EnvironmentParametersChannel,
|
|
)
|
|
|
|
|
|
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"
|
|
or val == "Environment/Group Cumulative Reward"
|
|
):
|
|
|
|
print(step, val, stats_summary.aggregated_value)
|
|
self._last_reward_summary[category] = stats_summary.aggregated_value
|
|
|
|
|
|
# The reward processor is passed as an argument to _check_environment_trains.
|
|
# It is applied to the list of 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 should 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(f"Last {last_n_rewards} rewards:", rewards_to_use)
|
|
return np.array(rewards[-last_n_rewards:], dtype=np.float32).mean()
|
|
|
|
|
|
def check_environment_trains(
|
|
env,
|
|
trainer_config,
|
|
reward_processor=default_reward_processor,
|
|
env_parameter_manager=None,
|
|
success_threshold=0.9,
|
|
env_manager=None,
|
|
training_seed=None,
|
|
):
|
|
if env_parameter_manager is None:
|
|
env_parameter_manager = EnvironmentParameterManager()
|
|
# Create controller and begin training.
|
|
with tempfile.TemporaryDirectory() as dir:
|
|
run_id = "id"
|
|
seed = 1337 if training_seed is None else training_seed
|
|
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, EnvironmentParametersChannel())
|
|
trainer_factory = TrainerFactory(
|
|
trainer_config=trainer_config,
|
|
output_path=dir,
|
|
train_model=True,
|
|
load_model=False,
|
|
seed=seed,
|
|
param_manager=env_parameter_manager,
|
|
multi_gpu=False,
|
|
)
|
|
|
|
tc = TrainerController(
|
|
trainer_factory=trainer_factory,
|
|
output_path=dir,
|
|
run_id=run_id,
|
|
param_manager=env_parameter_manager,
|
|
train=True,
|
|
training_seed=seed,
|
|
)
|
|
|
|
# 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)
|