import gym
from rlkit.envs.point_robot import PointEnv
import numpy as np
from rlkit.envs import ENVS
from rlkit.envs.wrappers import NormalizedBoxEnv
print(ENVS)
env = NormalizedBoxEnv(ENVS['point-robot']())
traj = []
obs = env.reset()
traj.append(obs)
while True:
action = env.action_space.sample()
# action = np.array([2.0, 2.0])
print(action)
obs, rew, d, _ = env.step(action)
print(obs, rew)
traj.append(obs)
env.render()
if d:
break
print(traj)
states = np.array(traj)
import numpy as np
import matplotlib
import numpy as np
import time
import argparse
from rlkit.envs.wrappers import NormalizedBoxEnv
parser = argparse.ArgumentParser()
parser.add_argument('--exp_name', type=str, default='Ant')
parser.add_argument('--ml', type=int, default=1000)
args = parser.parse_args()
import gym
env = NormalizedBoxEnv(gym.make(args.exp_name+'-v2'))
o = env.reset()
# print(env.observation_space.high)
max_path_length = args.ml
path_length = 0
done = False
c_r = 0.0
while (path_length < max_path_length) and (not done):
path_length += 1
a = env.action_space.sample()
o, r, done, _ = env.step(a)
c_r += r
env.render()
print("step: ",path_length)
print("o_max: ",np.max(o),np.argmax(o))
print("o_mean: ",np.mean(o))
print("a: ",a)
print('r: ',r)
print(done)
