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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# # Unity ML-Agents Toolkit
import logging
import csv
from time import time
LOGGER = logging.getLogger("mlagents.trainers")
FIELD_NAMES = [
"Brain name",
"Time to update policy",
"Time since start of training",
"Time for last experience collection",
"Number of experiences used for training",
"Mean return",
]
class TrainerMetrics:
"""
Helper class to track, write training metrics. Tracks time since object
of this class is initialized.
"""
def __init__(self, path: str, brain_name: str):
"""
:str path: Fully qualified path where CSV is stored.
:str brain_name: Identifier for the Brain which we are training
"""
self.path = path
self.brain_name = brain_name
self.rows = []
self.time_start_experience_collection = None
self.time_training_start = time()
self.last_buffer_length = None
self.last_mean_return = None
self.time_policy_update_start = None
self.delta_last_experience_collection = None
self.delta_policy_update = None
def start_experience_collection_timer(self):
"""
Inform Metrics class that experience collection is starting. Intended to be idempotent
"""
if self.time_start_experience_collection is None:
self.time_start_experience_collection = time()
def end_experience_collection_timer(self):
"""
Inform Metrics class that experience collection is done.
"""
if self.time_start_experience_collection:
curr_delta = time() - self.time_start_experience_collection
if self.delta_last_experience_collection is None:
self.delta_last_experience_collection = curr_delta
else:
self.delta_last_experience_collection += curr_delta
self.time_start_experience_collection = None
def add_delta_step(self, delta: float):
"""
Inform Metrics class about time to step in environment.
"""
if self.delta_last_experience_collection:
self.delta_last_experience_collection += delta
else:
self.delta_last_experience_collection = delta
def start_policy_update_timer(self, number_experiences: int, mean_return: float):
"""
Inform Metrics class that policy update has started.
:int number_experiences: Number of experiences in Buffer at this point.
:float mean_return: Return averaged across all cumulative returns since last policy update
"""
self.last_buffer_length = number_experiences
self.last_mean_return = mean_return
self.time_policy_update_start = time()
def _add_row(self, delta_train_start):
row = [self.brain_name]
row.extend(
format(c, ".3f") if isinstance(c, float) else c
for c in [
self.delta_policy_update,
delta_train_start,
self.delta_last_experience_collection,
self.last_buffer_length,
self.last_mean_return,
]
)
self.delta_last_experience_collection = None
self.rows.append(row)
def end_policy_update(self):
"""
Inform Metrics class that policy update has started.
"""
if self.time_policy_update_start:
self.delta_policy_update = time() - self.time_policy_update_start
else:
self.delta_policy_update = 0
delta_train_start = time() - self.time_training_start
LOGGER.debug(
" Policy Update Training Metrics for {}: "
"\n\t\tTime to update Policy: {:0.3f} s \n"
"\t\tTime elapsed since training: {:0.3f} s \n"
"\t\tTime for experience collection: {:0.3f} s \n"
"\t\tBuffer Length: {} \n"
"\t\tReturns : {:0.3f}\n".format(
self.brain_name,
self.delta_policy_update,
delta_train_start,
self.delta_last_experience_collection,
self.last_buffer_length,
self.last_mean_return,
)
)
self._add_row(delta_train_start)
def write_training_metrics(self):
"""
Write Training Metrics to CSV
"""
with open(self.path, "w") as file:
writer = csv.writer(file)
writer.writerow(FIELD_NAMES)
for row in self.rows:
writer.writerow(row)