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video_sim.py
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"""
Run before using this file:
unset LD_PRELOAD
"""
import argparse
import sys
import os
from datetime import datetime
import gym
from gym import wrappers, logger
class RandomAgent(object):
"""The world's simplest agent!"""
def __init__(self, action_space):
self.action_space = action_space
def act(self, observation, reward, done):
return self.action_space.sample()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description=None)
# parser.add_argument('env_id', nargs='?', default='FetchReach-v1', help='Select the environment to run')
parser.add_argument('env_id', nargs='?', default='Gen3-v0', help='Select the environment to run')
args = parser.parse_args()
# You can set the level to logger.DEBUG or logger.WARN if you
# want to change the amount of output.
logger.set_level(logger.INFO)
env = gym.make(args.env_id)
# You provide the directory to write to (can be an existing
# directory, including one with existing data -- all monitor files
# will be namespaced). You can also dump to a tempdir if you'd
# like: tempfile.mkdtemp().
# today = datetime.now()
# print(datetime.now() .strftime("%y%m%d%H%M%S"))
outdir = os.path.dirname(os.path.abspath(__file__)) + '/results/random-agent-results/' + datetime.now() .strftime("%m%d%H%M")
env = wrappers.Monitor(env, directory=outdir, force=True)
env.seed(0)
agent = RandomAgent(env.action_space)
episode_count = 100
reward = 0
done = False
for i in range(episode_count):
ob = env.reset()
while True:
action = agent.act(ob, reward, done)
ob, reward, done, _ = env.step(action)
#env.sim.render(500, 500) # does not work yet
if done:
break
# Note there's no env.render() here. But the environment still can open window and
# render if asked by env.monitor: it calls env.render('rgb_array') to record video.
# Video is not recorded every episode, see capped_cubic_video_schedule for details.
# if the number of episodes are smaller than 1k, it only saves the episode with an id of id**3
# otherwise it only saves the episode with an id of multiples of 1k
# Close the env and write monitor result info to disk
env.close()