def _play(self, planning_data):
if self.args.num_expert_episode_to_save > 0 and \
self._previous_reward > self._env_solved_reward and \
self._worker_id == 0:
start_save_episode = True
logger.info('Last episodic reward: %.4f' % self._previous_reward)
logger.info('Minimum reward of %.4f is needed to start saving' %
self._env_solved_reward)
logger.info('[SAVING] Worker %d will record its episode data' %
self._worker_id)
else:
start_save_episode = False
if self.args.num_expert_episode_to_save > 0 \
and self._worker_id == 0:
logger.info('Last episodic reward: %.4f' %
self._previous_reward)
logger.info(
'Minimum reward of %.4f is needed to start saving' %
self._env_solved_reward)
traj_episode = play_episode_with_env(
self._env, self._act, {
'use_random_action': planning_data['use_random_action'],
'record_flag': start_save_episode,
'num_episode': self.args.num_expert_episode_to_save,
'data_name': self.args.task + '_' + self.args.exp_id
})
self._previous_reward = np.sum(traj_episode['rewards'])
return traj_episode
def _play(self, use_random_action=False):
# provide the environment, the policy function @self._act
traj_episode = play_episode_with_env(
self._env, self._act, {'use_random_action': use_random_action}
)
return traj_episode
def _play(self, planning_data):
'''
# TODO NOTE:
var_list = self._network['policy'][0]._trainable_var_list
print('')
for var in var_list:
print(var.name)
# print(var.name, self._session.run(var)[-1])
'''
if planning_data['use_true_env']:
traj_episode = play_episode_with_env(
self._env, self._act,
{'use_random_action': planning_data['use_random_action']})
else:
traj_episode = play_episode_with_env(
self._fake_env, self._act,
{'use_random_action': planning_data['use_random_action']})
return traj_episode