def get_time_to_first_contact(env, policy, is_random=False, num_trajs=100):
import itertools
time_contact = []
if is_random:
from rllab.policies.uniform_control_policy import UniformControlPolicy
policy = UniformControlPolicy(env.spec)
print("Using {}".format(policy))
for traj_i in range(num_trajs):
obs = env.reset()
print("Start traj {}".format(traj_i))
for t in itertools.count():
action, _ = policy.get_action(obs)
obs, reward, done, env_info = env.step(action)
if env_info['contact_reward'] > 0 or done:
time_contact.append(t)
break
# plt.hist(time_contact)
# plt.title("Time to first contact over {} trajectories".format(num_trajs))
# plt.show()
data_path = input("Where do you want to save it? \n")
np.save(data_path, time_contact)
print("Data saved")
print(
"Mean time to first contact: {}, median:{}, std:{} for {}, ({} trajectories)"
.format(np.mean(time_contact), np.median(time_contact),
np.std(time_contact), policy, num_trajs))
def episode_reward(env, policy, is_random=False):
import itertools
mean_reward = []
if is_random:
from rllab.policies.uniform_control_policy import UniformControlPolicy
policy = UniformControlPolicy(env.spec)
print ("Using {}".format(policy))
for traj_i in range(num_trajs):
obs = env.reset()
print ("Start traj {}".format(traj_i))
rewards
for t in itertools.count():
action, _ = policy.get_action(obs)
obs, reward, done, env_info = env.step(action)
if done:
break
plt.his
print ("Mean time to first contact: {} for {}, ({} trajectories)".format(np.mean(time_contact), policy, num_trajs))
def test_state_hist(env):
policy = UniformControlPolicy(env.spec)
_states = []
o = env.reset()
try:
while True:
_states.append(o)
a, _ = policy.get_action(o)
next_o, r, d, env_info = env.step(a)
if d:
o = env.reset()
else:
o = next_o
except KeyboardInterrupt:
states = np.asarray(_states)
save_path = '/Users/dianchen/state.npy'
np.save(save_path, states)
# pickle.dump(states, save_path)
print ("State samples saved to {}".format(save_path))