def custom_env(env_config):
''' Create an env stub for policy training. Only the
action_space and observation_space attributes need to be defined,
no other env functions are needed (e.g. step(), reset(), etc). '''
env = gym.Env()
env.action_space = agent_action_space
env.observation_space = agent_obs_space
return env
def setUp(self) -> None:
self.obs_idx = 0
self.dummy_env = gym.Env()
self.dummy_env.observation_space = gym.spaces.Box(low=0,
high=255,
shape=(84, 84, 3),
dtype=np.uint8)
self.obs_buffer = ObservationBufferWrapper(self.dummy_env,
obs_buffer_depth=3)
self.visualize_buffer()
def test_load_cell_raises_exception_on_non_loadable_cell(self):
save_function = mock.MagicMock()
load_function = mock.MagicMock()
core_env = gym.Env()
env = save_loadable.SaveLoadableWrapper(core_env, save_function,
load_function)
test_cell = base_returning_info.BaseReturningInfo()
policy = load_policy.LoadPolicy(env)
with self.assertRaisesRegexp(
TypeError, "LoadableCellInfo.*got.*BaseReturningInfo"):
policy.return_to_cell(test_cell)
def test(rank, params, shared_model):
torch.manual_seed(params.seed + rank) # asynchronizing the test agent
env = gym.Env(params.env_name,
video=True) # running an environment with a video
env.seed(params.seed + rank) # asynchronizing the environment
model = AC_netwok(env.observation_space.shape[0],
env.action_space) # creating one model
model.eval(
) # putting the model in "eval" model because it won't be trained
state = env.reset() # getting the input images as numpy arrays
state = torch.from_numpy(state) # converting them into torch tensors
reward_sum = 0 # initializing the sum of rewards to 0
done = True # initializing done to True
start_time = time.time(
) # getting the starting time to measure the computation time
actions = deque(
maxlen=100) # cf https://pymotw.com/2/collections/deque.html
episode_length = 0 # initializing the episode length to 0
while True: # repeat
episode_length += 1 # incrementing the episode length by one
if done: # synchronizing with the shared model (same as train.py)
model.load_state_dict(shared_model.state_dict())
cx = Variable(torch.zeros(1, 256), volatile=True)
hx = Variable(torch.zeros(1, 256), volatile=True)
else:
cx = Variable(cx.data, volatile=True)
hx = Variable(hx.data, volatile=True)
value, action_value, (hx, cx) = model(
(Variable(state.unsqueeze(0), volatile=True), (hx, cx)))
prob = F.softmax(action_value)
action = prob.max(1)[1].data.numpy(
) # the test agent does not explore, it directly plays the best action
state, reward, done, _ = env.step(action[
0,
0]) # done = done or episode_length >= params.max_episode_length
reward_sum += reward
if done: # printing the results at the end of each part
print("Time {}, episode reward {}, episode length {}".format(
time.strftime("%Hh %Mm %Ss",
time.gmtime(time.time() - start_time)),
reward_sum, episode_length))
reward_sum = 0 # reinitializing the sum of rewards
episode_length = 0 # reinitializing the episode length
actions.clear() # reinitializing the actions
state = env.reset() # reinitializing the environment
time.sleep(
60
) # doing a one minute break to let the other agents practice (if the game is done)
state = torch.from_numpy(state) # new state and we continue
def test_gym_loads_cell_using_saved_snapshot(self):
save_function = mock.MagicMock()
load_function = mock.MagicMock()
core_env = gym.Env()
env = save_loadable.SaveLoadableWrapper(core_env, save_function,
load_function)
cell = loadable_cell_info.LoadableCellInfo(
snapshot_data="snapshot_data")
policy = load_policy.LoadPolicy(env)
policy.return_to_cell(cell)
load_function.assert_called_once_with(core_env, "snapshot_data")
import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
import time
import subprocess
# See user manual https://usermanual.wiki/Document/pybullet20quickstart20guide.479068914.pdf
import pybullet as p
import pybullet_data
from pkg_resources import parse_version
from servorobots.components.dc_motor import GearedDcMotor
from servorobots.components.dc_motor import TimestampInput
logger = logging.getLogger(__name__)
gym.Env()
class qt:
def q_mult(q1, q2):
x1, y1, z1, w1 = q1
x2, y2, z2, w2 = q2
w = w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2
x = w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2
y = w1 * y2 + y1 * w2 + z1 * x2 - x1 * z2
z = w1 * z2 + z1 * w2 + x1 * y2 - y1 * x2
return x, y, z, w
def q_conjugate(q):
x, y, z, w = q
return (-x, -y, -z, w)
if __name__ == "__main__":
action_space = gym.spaces.Dict({
"attack":
gym.spaces.Discrete(2),
"back":
gym.spaces.Discrete(2),
"forward":
gym.spaces.Discrete(2),
"camera":
gym.spaces.Box(low=-180, high=180, shape=(2, ), dtype=np.float32)
})
action_space = translate_action_space(action_space)
print(repr(action_space))
env = gym.Env()
env.action_space = action_space
env2 = DictToMultiDiscreteActionWrapper(env)
try:
env2.step(env2.action_space.sample())
except NotImplementedError:
pass
env = gym.Env()
env.action_space = action_space
env2 = DictToDiscreteActionWrapper(env)
try:
env2.step(env2.action_space.sample())
except NotImplementedError:
pass
import gym
env=gym.Env("CartPole")
# hello
#hello