def sim(file="",
isdeterm=True,
speedup=1,
max_path_length=200,
animated=True,
wrap_env=False):
# If the snapshot file use tensorflow, do:
# import tensorflow as tf
# with tf.Session():
# [rest of the code]
if isdeterm:
rollout_fn = deterministic_rollout
else:
rollout_fn = rollout
with tf.Session() as sess:
data = joblib.load(file)
policy = data['policy']
if not wrap_env:
env = data['env']
else:
env = VaryMassRolloutWrapper(data['env'])
print("m0: ", env.env.m0)
print("mf: ", env.env.mf)
# env = TfEnv(GymEnv("MyPendulum-v0", record_video=False))
# print("mass: ", env._wrapped_env.env.env.m)
while True:
path = rollout_fn(env,
policy,
max_path_length=max_path_length,
animated=animated,
speedup=speedup,
always_return_paths=True)
import pdb
pdb.set_trace()
if not query_yes_no('Continue simulation?'):
break
def main():
env = normalize(load_class('aa_simulation.envs.empty_env',
Env, ["rllab", "envs"])())
state = env.reset()
env.render()
t = 0
max_t = 100
while True:
while t < max_t:
if t < max_t / 2:
action = np.array([1.0, np.deg2rad(15)])
else:
action = np.array([1.0, -np.deg2rad(15)])
nextstate, reward, done, _ = env.step(action)
env.render()
t += 1
if query_yes_no('Continue simulation?'):
t = 0
else:
break
type=bool,
default=False,
help='Whether or not to prompt for more sim')
args = parser.parse_args()
max_tries = 10
tri = 0
while True:
tri += 1
with tf.Session() as sess:
data = joblib.load(args.file)
policy = data['policy']
env = data['env']
while True:
path = rollout(env,
policy,
max_path_length=args.max_path_length,
animated=True,
speedup=args.speedup,
video_filename=args.video_filename)
if args.prompt:
if not query_yes_no('Continue simulation?'):
break
else:
break
#import pdb; pdb.set_trace()
if len(path['rewards']) < args.max_path_length and tri >= max_tries:
tf.reset_default_graph()
continue
break
help='Max length of rollout')
parser.add_argument('--speedup', type=float, default=1,
help='Speedup')
parser.add_argument('--video_filename', type=str,
help='path to the out video file')
parser.add_argument('--prompt', type=bool, default=False,
help='Whether or not to prompt for more sim')
args = parser.parse_args()
max_tries = 10
tri = 0
while True:
tri += 1
with tf.Session() as sess:
data = joblib.load(args.file)
policy = data['policy']
env = data['env']
while True:
path = rollout(env, policy, max_path_length=args.max_path_length,
animated=True, speedup=args.speedup, video_filename=args.video_filename)
if args.prompt:
if not query_yes_no('Continue simulation?'):
break
else:
break
#import pdb; pdb.set_trace()
if len(path['rewards']) < args.max_path_length and tri >= max_tries:
tf.reset_default_graph()
continue
break
