def save(self, path):
"""Save model to a pickle located at `path`"""
with tempfile.TemporaryDirectory() as td:
U.save_state(os.path.join(td, "model"))
arc_name = os.path.join(td, "packed.zip")
with zipfile.ZipFile(arc_name, 'w') as zipf:
for root, dirs, files in os.walk(td):
for fname in files:
file_path = os.path.join(root, fname)
if file_path != arc_name:
zipf.write(file_path,
os.path.relpath(file_path, td))
with open(arc_name, "rb") as f:
model_data = f.read()
with open(path, "wb") as f:
dill.dump((model_data, self._act_params), f)
def evaluate(step, episode_number):
global max_eval_reward_mean, model_saved
print('Evaluate...')
eval_reward_sum = 0.0
# Run evaluation episodes
for eval_episode in range(n_eval_episodes):
obs = env.reset()
done = False
while not done:
# Choose action
action_idxes = np.array(
act(np.array(obs)[None],
stochastic=False)) # deterministic
actions_greedy = action_idxes / num_action_grains * actions_range + low
if eval_std == 0.0:
action = actions_greedy
else:
action = []
for index in range(len(actions_greedy)):
a_greedy = actions_greedy[index]
out_of_range_action = True
while out_of_range_action:
a_stoch = np.random.normal(loc=a_greedy,
scale=eval_std)
a_idx_stoch = np.rint(
(a_stoch + high[index]) /
actions_range[index] * num_action_grains)
if a_idx_stoch >= 0 and a_idx_stoch < num_actions_pad:
action.append(a_stoch)
out_of_range_action = False
# Step
obs, rew, done, _ = env.step(action)
eval_reward_sum += rew
# Average the rewards and log
eval_reward_mean = eval_reward_sum / n_eval_episodes
print(eval_reward_mean, 'over', n_eval_episodes, 'episodes')
with open(
"{}/{}_{}_{}_eval.csv".format(results_dir, method_name,
time_stamp, env_name),
"a") as eval_fw:
eval_writer = csv.writer(
eval_fw,
delimiter="\t",
lineterminator="\n",
)
eval_writer.writerow([episode_number, step, eval_reward_mean])
summary = sess.run(eval_summary,
feed_dict={eval_placeholder: eval_reward_mean})
log_writer.add_summary(summary, episode_number)
log_writer.flush()
if max_eval_reward_mean is None or eval_reward_mean > max_eval_reward_mean:
logger.log(
"Saving model due to mean eval increase: {} -> {}".format(
max_eval_reward_mean, eval_reward_mean))
U.save_state(model_file)
model_saved = True
max_eval_reward_mean = eval_reward_mean