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r"""Running a pre-trained ppo agent on minitaur_reactive_env."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(os.path.dirname(os.path.dirname(currentdir)))
print("parentdir=",parentdir)
os.sys.path.insert(0,parentdir)
import tensorflow as tf
from pybullet_envs.minitaur.agents.scripts import utility
import pybullet_data
from pybullet_envs.minitaur.envs import simple_ppo_agent
flags = tf.app.flags
FLAGS = tf.app.flags.FLAGS
LOG_DIR = os.path.join(pybullet_data.getDataPath(), "policies/ppo/minitaur_reactive_env")
CHECKPOINT = "model.ckpt-14000000"
def main(argv):
del argv # Unused.
config = utility.load_config(LOG_DIR)
policy_layers = config.policy_layers
value_layers = config.value_layers
env = config.env(render=True)
network = config.network
with tf.Session() as sess:
agent = simple_ppo_agent.SimplePPOPolicy(
sess,
env,
network,
policy_layers=policy_layers,
value_layers=value_layers,
checkpoint=os.path.join(LOG_DIR, CHECKPOINT))
sum_reward = 0
observation = env.reset()
while True:
action = agent.get_action([observation])
observation, reward, done, _ = env.step(action[0])
time.sleep(0.002)
sum_reward += reward
if done:
break
tf.logging.info("reward: %s", sum_reward)
if __name__ == "__main__":
tf.app.run(main)
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