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import json
import os
import csv
import numpy as np
from itertools import product
from src.smb.level import save_batch
from src.utils.filesys import getpath
############### Loggers for async environment ###############
class AsyncCsvLogger:
def __init__(self, target, rfunc, buffer_size=50):
self.rterms = tuple(term.get_name() for term in rfunc.terms)
self.cols = ('steps', *self.rterms, 'reward_sum', 'time', 'trans', 'updates', '')
self.buffer = []
self.buffer_size = buffer_size
self.ftarget = open(getpath(target), 'w', newline='')
self.writer = csv.writer(self.ftarget)
self.writer.writerow(self.cols)
def on_episode(self, **kwargs):
for rews in kwargs['rewss']:
rews_list = [sum(rews[key]) for key in self.rterms]
self.buffer.append([
kwargs['steps'], *rews_list, sum(rews_list),
kwargs['time'], kwargs['trans'], kwargs['updates']
])
self.__try_write()
if kwargs['close']:
self.close()
def __try_write(self):
if len(self.buffer) < self.buffer_size:
return
self.writer.writerows(self.buffer)
self.ftarget.flush()
self.buffer.clear()
def close(self):
self.writer.writerows(self.buffer)
self.ftarget.close()
pass
class AsyncStdLogger:
def __init__(self, rfunc, itv=2000, path=''):
self.rterms = tuple(term.get_name() for term in rfunc.terms)
self.rews = {rterm: 0. for rterm in self.rterms}
self.n = 0
self.itv = itv
self.horizon = itv
if not len(path):
self.f = None
else:
self.f = open(getpath(path), 'w')
self.last_steps = 0
self.last_trans = 0
self.last_updates = 0
self.buffer = []
pass
def on_episode(self, **kwargs):
newrews = {k: self.rews[k] for k in self.rterms}
for rews, k in product(kwargs['rewss'], self.rterms):
newrews[k] = newrews[k] + sum(rews[k])
self.rews = newrews
self.n += len(kwargs['rewss'])
if kwargs['steps'] >= self.horizon or kwargs['close']:
self.__output(**kwargs)
self.horizon += self.itv
self.rews = {k: 0 for k in self.rews.keys()}
self.n = 0
self.last_steps = kwargs['steps']
self.last_trans = kwargs['trans']
self.last_updates = kwargs['updates']
if kwargs['close'] and self.f is not None:
self.f.close()
def __output(self, **kwargs):
steps = kwargs['steps']
if kwargs['close']:
head = '-' * 20 + 'Closing rollouts' + '-' * 20
else:
head = '-' * 20 + f'Rollout of {self.last_steps}-{steps} steps' + '-' * 20
self.buffer.append(head)
rsum = 0
for t in self.rterms:
v = 0 if self.n == 0 else self.rews[t] / self.n
rsum += v
self.buffer.append(f'{t}: {v:.2f}')
self.buffer.append(f'Reward sum: {rsum: .2f}')
self.buffer.append('Time elapsed: %.1fs' % kwargs['time'])
self.buffer.append('Transitions collected: %d (%d in total)' % (kwargs['trans'] - self.last_trans, kwargs['trans']))
self.buffer.append('Number of updates: %d (%d in total)' % (kwargs['updates']- self.last_updates, kwargs['updates']))
if self.f is None:
print('\n'.join(self.buffer) + '\n')
else:
self.f.write('\n'.join(self.buffer) + '\n')
self.f.flush()
self.buffer.clear()
pass
def __reset(self):
pass
class GenResLogger:
def __init__(self, root_path, k, itv=5000):
self.k = k
self.itv = itv
self.horizon = 0
self.path = getpath(f'{root_path}/gen_log')
os.makedirs(self.path, exist_ok=True)
def on_episode(self, env, agent, steps):
if steps >= self.horizon:
lvls, vectraj = env.generate_levels(agent, self.k)
# np.save(f'{self.path}/step{steps}', vectraj)
if len(lvls):
save_batch(lvls, f'{self.path}/step{steps}')
self.horizon += self.itv
pass
pass
############### Loggers for sync environment, from https://github.com/SUSTechGameAI/MFEDRL ###############
class InfoCollector:
ignored_keys = {'episode', 'terminal_observation'}
save_itv = 1000
def __init__(self, path, log_itv=100, log_targets=None):
self.data = []
self.path = path
self.msg_itv = log_itv
self.time_before_save = InfoCollector.save_itv
self.msg_ptr = 0
self.log_targets = [] if log_targets is None else log_targets
if 'file' in log_targets:
with open(f'{self.path}/log.txt', 'w') as f:
f.write('')
self.recent_time = 0
def on_step(self, dones, infos):
for done, info in zip(dones, infos):
if done:
self.data.append({
key: val for key, val in info.items()
if key not in InfoCollector.ignored_keys and 'reward_list' not in key
})
self.time_before_save -= 1
if self.time_before_save <= 0:
with open(f'{self.path}/ep_infos.json', 'w') as f:
json.dump(self.data, f)
self.time_before_save += InfoCollector.save_itv
if self.log_targets and 0 < self.msg_itv <= (len(self.data) - self.msg_ptr):
keys = set(self.data[-1].keys()) - {'TotalSteps', 'TimePassed', 'TotalScore', 'EpLen'}
msg = '%sTotal steps: %d%s\n' % ('-' * 16, self.data[-1]['TotalSteps'], '-' * 16)
msg += 'Time passed: %ds\n' % self.data[-1]['TimePassed']
t = self.data[-1]['TimePassed'] - self.recent_time
self.recent_time = self.data[-1]['TimePassed']
f = sum(item['EpLength'] for item in self.data[self.msg_ptr:])
msg += 'fps: %.3g\n' % (f/t)
for key in keys:
values = [item[key] for item in self.data[self.msg_ptr:]]
values = np.array(values)
msg += '%s: %.2f +- %.2f\n' % (key, values.mean(), values.std())
values = [item['TotalScore'] for item in self.data[self.msg_ptr:]]
values = np.array(values)
msg += 'TotalScore: %.2f +- %.2f\n' % (values.mean(), values.std())
if 'file' in self.log_targets:
with open(f'{self.path}/log.txt', 'a') as f:
f.write(msg + '\n')
if 'std' in self.log_targets:
print(msg)
self.msg_ptr = len(self.data)
pass
def close(self):
with open(self.path, 'w') as f:
json.dump(self.data, f)
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