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generate_ant_maze_datasets.py
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generate_ant_maze_datasets.py
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import numpy as np
import pickle
import gzip
import h5py
import argparse
from d4rl.locomotion import maze_env, ant, swimmer
from d4rl.locomotion.wrappers import NormalizedBoxEnv
import torch
from PIL import Image
import os
def reset_data():
return {'observations': [],
'actions': [],
'terminals': [],
'timeouts': [],
'rewards': [],
'infos/goal': [],
'infos/qpos': [],
'infos/qvel': [],
}
def append_data(data, s, a, r, tgt, done, timeout, env_data):
data['observations'].append(s)
data['actions'].append(a)
data['rewards'].append(r)
data['terminals'].append(done)
data['timeouts'].append(timeout)
data['infos/goal'].append(tgt)
data['infos/qpos'].append(env_data.qpos.ravel().copy())
data['infos/qvel'].append(env_data.qvel.ravel().copy())
def npify(data):
for k in data:
if k in ['terminals', 'timeouts']:
dtype = np.bool_
else:
dtype = np.float32
data[k] = np.array(data[k], dtype=dtype)
def load_policy(policy_file):
data = torch.load(policy_file)
policy = data['exploration/policy'].to('cpu')
env = data['evaluation/env']
print("Policy loaded")
return policy, env
def save_video(save_dir, file_name, frames, episode_id=0):
filename = os.path.join(save_dir, file_name+ '_episode_{}'.format(episode_id))
if not os.path.exists(filename):
os.makedirs(filename)
num_frames = frames.shape[0]
for i in range(num_frames):
img = Image.fromarray(np.flipud(frames[i]), 'RGB')
img.save(os.path.join(filename, 'frame_{}.png'.format(i)))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--noisy', action='store_true', help='Noisy actions')
parser.add_argument('--maze', type=str, default='umaze', help='Maze type. umaze, medium, or large')
parser.add_argument('--num_samples', type=int, default=int(1e6), help='Num samples to collect')
parser.add_argument('--env', type=str, default='Ant', help='Environment type')
parser.add_argument('--policy_file', type=str, default='policy_file', help='file_name')
parser.add_argument('--max_episode_steps', default=1000, type=int)
parser.add_argument('--video', action='store_true')
parser.add_argument('--multi_start', action='store_true')
parser.add_argument('--multigoal', action='store_true')
args = parser.parse_args()
if args.maze == 'umaze':
maze = maze_env.U_MAZE
elif args.maze == 'medium':
maze = maze_env.BIG_MAZE
elif args.maze == 'large':
maze = maze_env.HARDEST_MAZE
elif args.maze == 'umaze_eval':
maze = maze_env.U_MAZE_EVAL
elif args.maze == 'medium_eval':
maze = maze_env.BIG_MAZE_EVAL
elif args.maze == 'large_eval':
maze = maze_env.HARDEST_MAZE_EVAL
else:
raise NotImplementedError
if args.env == 'Ant':
env = NormalizedBoxEnv(ant.AntMazeEnv(maze_map=maze, maze_size_scaling=4.0, non_zero_reset=args.multi_start))
elif args.env == 'Swimmer':
env = NormalizedBoxEnv(swimmer.SwimmerMazeEnv(mmaze_map=maze, maze_size_scaling=4.0, non_zero_reset=args.multi_start))
else:
raise NotImplementedError
env.set_target()
s = env.reset()
act = env.action_space.sample()
done = False
# Load the policy
policy, train_env = load_policy(args.policy_file)
# Define goal reaching policy fn
def _goal_reaching_policy_fn(obs, goal):
goal_x, goal_y = goal
obs_new = obs[2:-2]
goal_tuple = np.array([goal_x, goal_y])
# normalize the norm of the relative goals to in-distribution values
goal_tuple = goal_tuple / np.linalg.norm(goal_tuple) * 10.0
new_obs = np.concatenate([obs_new, goal_tuple], -1)
return policy.get_action(new_obs)[0], (goal_tuple[0] + obs[0], goal_tuple[1] + obs[1])
data = reset_data()
# create waypoint generating policy integrated with high level controller
data_collection_policy = env.create_navigation_policy(
_goal_reaching_policy_fn,
)
if args.video:
frames = []
ts = 0
num_episodes = 0
for _ in range(args.num_samples):
act, waypoint_goal = data_collection_policy(s)
if args.noisy:
act = act + np.random.randn(*act.shape)*0.2
act = np.clip(act, -1.0, 1.0)
ns, r, done, info = env.step(act)
timeout = False
if ts >= args.max_episode_steps:
timeout = True
#done = True
append_data(data, s[:-2], act, r, env.target_goal, done, timeout, env.physics.data)
if len(data['observations']) % 10000 == 0:
print(len(data['observations']))
ts += 1
if done or timeout:
done = False
ts = 0
s = env.reset()
env.set_target_goal()
if args.video:
frames = np.array(frames)
save_video('./videos/', args.env + '_navigation', frames, num_episodes)
num_episodes += 1
frames = []
else:
s = ns
if args.video:
curr_frame = env.physics.render(width=500, height=500, depth=False)
frames.append(curr_frame)
if args.noisy:
fname = args.env + '_maze_%s_noisy_multistart_%s_multigoal_%s.hdf5' % (args.maze, str(args.multi_start), str(args.multigoal))
else:
fname = args.env + 'maze_%s_multistart_%s_multigoal_%s.hdf5' % (args.maze, str(args.multi_start), str(args.multigoal))
dataset = h5py.File(fname, 'w')
npify(data)
for k in data:
dataset.create_dataset(k, data=data[k], compression='gzip')
if __name__ == '__main__':
main()