def run_eval_loop(sample_stochastically=True):
start_time = time.time()
prefix = 'stochastic_' if sample_stochastically else ''
for i in range(num_episodes):
obs = env.reset()
video.init(enabled=(i == 0))
done = False
episode_reward = 0
while not done:
# center crop image
if args.encoder_type == 'pixel' and 'crop' in args.data_augs:
obs = utils.center_crop_image(obs, args.image_size)
if args.encoder_type == 'pixel' and 'translate' in args.data_augs:
# first crop the center with pre_image_size
obs = utils.center_crop_image(
obs, args.pre_transform_image_size)
# then translate cropped to center
obs = utils.center_translate(obs, args.image_size)
with utils.eval_mode(agent):
if sample_stochastically:
action = agent.sample_action(obs / 255.)
else:
action = agent.select_action(obs / 255.)
obs, reward, done, _ = env.step(action)
video.record(env)
episode_reward += reward
video.save('%d.mp4' % step)
L.log('eval/' + prefix + 'episode_reward', episode_reward, step)
all_ep_rewards.append(episode_reward)
L.log('eval/' + prefix + 'eval_time', time.time() - start_time, step)
mean_ep_reward = np.mean(all_ep_rewards)
best_ep_reward = np.max(all_ep_rewards)
std_ep_reward = np.std(all_ep_rewards)
L.log('eval/' + prefix + 'mean_episode_reward', mean_ep_reward, step)
L.log('eval/' + prefix + 'best_episode_reward', best_ep_reward, step)
filename = args.work_dir + '/' + args.domain_name + '--' + args.task_name + '-' + args.data_augs + '--s' + str(
args.seed) + '--eval_scores.npy'
key = args.domain_name + '-' + args.task_name + '-' + args.data_augs
try:
log_data = np.load(filename, allow_pickle=True)
log_data = log_data.item()
except:
log_data = {}
if key not in log_data:
log_data[key] = {}
log_data[key][step] = {}
log_data[key][step]['step'] = step
log_data[key][step]['mean_ep_reward'] = mean_ep_reward
log_data[key][step]['max_ep_reward'] = best_ep_reward
log_data[key][step]['std_ep_reward'] = std_ep_reward
log_data[key][step]['env_step'] = step * args.action_repeat
np.save(filename, log_data)
return log_data[key][step]
def run_eval_loop(sample_stochastically=True):
start_time = time.time()
prefix = "stochastic_" if sample_stochastically else ""
for i in range(num_episodes):
obs = env.reset()
video.init(enabled=(i == 0))
done = False
episode_reward = 0
while not done:
# center crop image
if args.encoder_type == "pixel" and "crop" in args.data_augs:
obs = utils.center_crop_image(obs, args.image_size)
if args.encoder_type == "pixel" and "translate" in args.data_augs:
# first crop the center with pre_image_size
obs = utils.center_crop_image(
obs, args.pre_transform_image_size)
# then translate cropped to center
obs = utils.center_translate(obs, args.image_size)
with utils.eval_mode(agent):
if sample_stochastically:
action = agent.sample_action(obs / 255.0)
else:
action = agent.select_action(obs / 255.0)
obs, reward, done, _ = env.step(action)
video.record(env)
episode_reward += reward
video.save("%d.mp4" % step)
L.log("eval/" + prefix + "episode_reward", episode_reward, step)
all_ep_rewards.append(episode_reward)
L.log("eval/" + prefix + "eval_time", time.time() - start_time, step)
mean_ep_reward = np.mean(all_ep_rewards)
best_ep_reward = np.max(all_ep_rewards)
std_ep_reward = np.std(all_ep_rewards)
L.log("eval/" + prefix + "mean_episode_reward", mean_ep_reward, step)
L.log("eval/" + prefix + "best_episode_reward", best_ep_reward, step)
filename = (args.work_dir + "/" + args.domain_name + "--" +
args.task_name + "-" + args.data_augs + "--s" +
str(args.seed) + "--eval_scores.npy")
key = args.domain_name + "-" + args.task_name + "-" + args.data_augs
try:
log_data = np.load(filename, allow_pickle=True)
log_data = log_data.item()
except:
log_data = {}
if key not in log_data:
log_data[key] = {}
log_data[key][step] = {}
log_data[key][step]["step"] = step
log_data[key][step]["mean_ep_reward"] = mean_ep_reward
log_data[key][step]["max_ep_reward"] = best_ep_reward
log_data[key][step]["std_ep_reward"] = std_ep_reward
log_data[key][step]["env_step"] = step * args.action_repeat
np.save(filename, log_data)
def run_eval_loop2(sample_stochastically=True,
cor_func="no_cor",
cor_sev=1):
cor = Corruptor(cor_func=cor_func, severity=cor_sev)
start_time = time.time()
prefix = 'stochastic_' if sample_stochastically else ''
all_ep_rewards = []
for i in range(num_episodes):
obs = env.reset()
obs = cor.corrupt_stacked_images(
obs, args.frame_stack) # added corruption after env
done = False
episode_reward = 0
while not done:
# center crop image
if args.encoder_type == 'pixel' and 'crop' in args.data_augs:
obs = utils.center_crop_image(obs, args.image_size)
if args.encoder_type == 'pixel' and 'translate' in args.data_augs:
# first crop the center with pre_image_size
obs = utils.center_crop_image(
obs, args.pre_transform_image_size)
# then translate cropped to center
obs = utils.center_translate(obs, args.image_size)
with utils.eval_mode(agent):
if sample_stochastically:
action = agent.sample_action(obs / 255.)
else:
action = agent.select_action(obs / 255.)
obs, reward, done, _ = env.step(action)
obs = cor.corrupt_stacked_images(
obs, args.frame_stack) # added corruption after env
episode_reward += reward
all_ep_rewards.append(episode_reward)
mean_ep_reward = np.mean(all_ep_rewards)
best_ep_reward = np.max(all_ep_rewards)
std_ep_reward = np.std(all_ep_rewards)
end_time = time.time()
return step, mean_ep_reward, best_ep_reward, std_ep_reward, end_time - start_time