def get_train_log(self, optim_infos, traj=None):
log_info = dict()
vector_keys = set()
scalar_keys = set()
for oinf in optim_infos:
for key in oinf.keys():
if 'vec_' in key:
vector_keys.add(key)
else:
scalar_keys.add(key)
for key in scalar_keys:
log_info[key] = np.mean(
[inf[key] for inf in optim_infos if key in inf])
for key in vector_keys:
k_stats = get_list_stats(
[inf[key] for inf in optim_infos if key in inf])
for sk, sv in k_stats.items():
log_info[f'{key}/' + sk] = sv
if traj is not None:
actions_stats = get_list_stats(traj.actions)
for sk, sv in actions_stats.items():
log_info['rollout_action/' + sk] = sv
log_info['rollout_steps_per_iter'] = traj.total_steps
ep_returns_stats = get_list_stats(self.runner.train_ep_return)
for sk, sv in ep_returns_stats.items():
log_info['episode_return/' + sk] = sv
train_log_info = dict()
for key, val in log_info.items():
train_log_info['train/' + key] = val
return train_log_info
def get_train_log(self, optim_infos, traj):
log_info = dict()
for key in optim_infos[0].keys():
log_info[key] = np.mean(
[inf[key] for inf in optim_infos if key in inf])
t1 = time.perf_counter()
actions_stats = get_list_stats(traj.actions)
for sk, sv in actions_stats.items():
log_info['rollout_action/' + sk] = sv
log_info['optim_time'] = t1 - self.optim_stime
log_info['rollout_steps_per_iter'] = traj.total_steps
# log infos
dones = traj.dones
for key in traj.infos[0][0].keys():
#print("key", key)
if "final" in key:
all_finals = []
finals = np.array([[
step_data.info[i][key] for i in range(len(step_data.info))
] for step_data in traj.traj_data])
epfinals = []
for i in range(dones.shape[1]):
di = dones[:, i]
if not np.any(di):
epfinals.append(finals[-1, i])
else:
done_idx = np.where(di)[0]
t = 0
for idx in done_idx:
epfinals.append(finals[idx, i])
t = idx + 1
info_list = epfinals
else:
info_list = [
tuple([info.get(key, 0) for info in infos])
for infos in traj.infos
]
#print("info list[0]", info_list[0])
try:
info_stats = get_list_stats(info_list)
for sk, sv in info_stats.items():
log_info['rollout_{}/'.format(key) + sk] = sv
except Exception:
continue
ep_returns = list(chain(*traj.episode_returns))
for epr in ep_returns:
self.train_ep_return.append(epr)
ep_returns_stats = get_list_stats(self.train_ep_return)
for sk, sv in ep_returns_stats.items():
log_info['episode_return/' + sk] = sv
train_log_info = dict()
for key, val in log_info.items():
train_log_info['train/' + key] = val
# histogram_log = {'histogram': {'rollout_action': traj.actions}}
# self.tf_logger.save_dict(histogram_log, step=self.cur_step)
return train_log_info
def get_train_log(self, optim_infos):
log_info = dict()
vector_keys = set()
scalar_keys = set()
for oinf in optim_infos:
for key in oinf.keys():
if 'vec_' in key:
vector_keys.add(key)
else:
scalar_keys.add(key)
for key in scalar_keys:
log_info[key] = np.mean(
[inf[key] for inf in optim_infos if key in inf])
for key in vector_keys:
k_stats = get_list_stats(
[inf[key] for inf in optim_infos if key in inf])
for sk, sv in k_stats.items():
log_info[f'{key}/' + sk] = sv
t1 = time.perf_counter()
log_info['optim_time'] = t1 - self.optim_stime
train_log_info = dict()
for key, val in log_info.items():
train_log_info['train/' + key] = val
return train_log_info
def eval(self, render=False, save_eval_traj=False, sample=True,
eval_num=1, sleep_time=0, smooth=True, no_tqdm=None):
time_steps = []
rets = []
lst_step_infos = []
if no_tqdm:
disable_tqdm = bool(no_tqdm)
else:
disable_tqdm = not cfg.alg.test
for idx in tqdm(range(eval_num), disable=disable_tqdm):
traj, _ = self.rollout_once(time_steps=cfg.alg.episode_steps,
return_on_done=True,
sample=cfg.alg.sample_action and sample,
render=render,
sleep_time=sleep_time,
render_image=save_eval_traj,
evaluation=True)
tsps = traj.steps_til_done.copy().tolist()
rewards = traj.raw_rewards
infos = traj.infos
for ej in range(traj.num_envs):
ret = np.sum(rewards[:tsps[ej], ej])
rets.append(ret)
lst_step_infos.append(infos[tsps[ej] - 1][ej])
time_steps.extend(tsps)
if save_eval_traj:
save_traj(traj, cfg.alg.eval_dir)
raw_traj_info = {'return': rets,
'episode_length': time_steps,
'lst_step_info': lst_step_infos}
log_info = dict()
for key, val in raw_traj_info.items():
if 'info' in key:
continue
val_stats = get_list_stats(val)
for sk, sv in val_stats.items():
log_info['eval/' + key + '/' + sk] = sv
if smooth:
if self.smooth_eval_return is None:
self.smooth_eval_return = log_info['eval/return/mean']
else:
self.smooth_eval_return = self.smooth_eval_return * self.smooth_tau
self.smooth_eval_return += (1 - self.smooth_tau) * log_info['eval/return/mean']
log_info['eval/smooth_return/mean'] = self.smooth_eval_return
if self.smooth_eval_return > self._best_eval_ret:
self._eval_is_best = True
self._best_eval_ret = self.smooth_eval_return
else:
self._eval_is_best = False
return log_info, raw_traj_info
