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visualize_skills.py
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visualize_skills.py
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import argparse
import numpy as np
import joblib
import tensorflow as tf
import os
from sac.misc import utils
from sac.policies.hierarchical_policy import FixedOptionPolicy
from sac.misc.sampler import rollouts
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('file', type=str, help='Path to the snapshot file.')
parser.add_argument('--max-path-length', '-l', type=int, default=100)
parser.add_argument('--speedup', '-s', type=float, default=1)
parser.add_argument('--deterministic', '-d', dest='deterministic',
action='store_true')
parser.add_argument('--no-deterministic', '-nd', dest='deterministic',
action='store_false')
parser.add_argument('--separate_videos', type=bool, default=False)
parser.set_defaults(deterministic=True)
args = parser.parse_args()
filename = os.path.splitext(args.file)[0] + '.avi'
best_filename = os.path.splitext(args.file)[0] + '_best.avi'
worst_filename = os.path.splitext(args.file)[0] + '_worst.avi'
path_list = []
reward_list = []
with tf.Session() as sess:
data = joblib.load(args.file)
policy = data['policy']
env = data['env']
num_skills = data['policy'].observation_space.flat_dim - data['env'].spec.observation_space.flat_dim
with policy.deterministic(args.deterministic):
for z in range(num_skills):
fixed_z_policy = FixedOptionPolicy(policy, num_skills, z)
new_paths = rollouts(env, fixed_z_policy,
args.max_path_length, n_paths=1,
render=True, render_mode='rgb_array')
path_list.append(new_paths)
total_returns = np.mean([path['rewards'].sum() for path in new_paths])
reward_list.append(total_returns)
if args.separate_videos:
base = os.path.splitext(args.file)[0]
end = '_skill_%02d.avi' % z
skill_filename = base + end
utils._save_video(new_paths, skill_filename)
if not args.separate_videos:
paths = [path for paths in path_list for path in paths]
utils._save_video(paths, filename)
print('Best reward: %d' % np.max(reward_list))
print('Worst reward: %d' % np.min(reward_list))
# Record extra long videos for best and worst skills:
best_z = np.argmax(reward_list)
worst_z = np.argmin(reward_list)
for (z, filename) in [(best_z, best_filename), (worst_z, worst_filename)]:
fixed_z_policy = FixedOptionPolicy(policy, num_skills, z)
new_paths = rollouts(env, fixed_z_policy,
3 * args.max_path_length, n_paths=1,
render=True, render_mode='rgb_array')
utils._save_video(new_paths, filename)