def _check_returned_values(env: gym.Env, observation_space: spaces.Space, action_space: spaces.Space) -> None:
"""
Check the returned values by the env when calling `.reset()` or `.step()` methods.
"""
# because env inherits from gym.Env, we assume that `reset()` and `step()` methods exists
obs = env.reset()
_check_obs(obs, observation_space, 'reset')
# Sample a random action
action = action_space.sample()
data = env.step(action)
assert len(data) == 4, "The `step()` method must return four values: obs, reward, done, info"
# Unpack
obs, reward, done, info = data
_check_obs(obs, observation_space, 'step')
# We also allow int because the reward will be cast to float
assert isinstance(reward, (float, int)), "The reward returned by `step()` must be a float"
assert isinstance(done, bool), "The `done` signal must be a boolean"
assert isinstance(info, dict), "The `info` returned by `step()` must be a python dictionary"
if isinstance(env, gym.GoalEnv):
# For a GoalEnv, the keys are checked at reset
assert reward == env.compute_reward(obs['achieved_goal'], obs['desired_goal'], info)
def _check_returned_values(env: gym.Env, observation_space: spaces.Space,
action_space: spaces.Space) -> None:
"""
Check the returned values by the env when calling `.reset()` or `.step()` methods.
"""
# because env inherits from gym.Env, we assume that `reset()` and `step()` methods exists
obs = env.reset()
if isinstance(observation_space, spaces.Dict):
assert isinstance(
obs,
dict), "The observation returned by `reset()` must be a dictionary"
for key in observation_space.spaces.keys():
try:
_check_obs(obs[key], observation_space.spaces[key], "reset")
except AssertionError as e:
raise AssertionError(f"Error while checking key={key}: " +
str(e))
else:
_check_obs(obs, observation_space, "reset")
# Sample a random action
action = action_space.sample()
data = env.step(action)
assert (
len(data) == 4
), "The `step()` method must return four values: obs, reward, done, info"
# Unpack
obs, reward, done, info = data
if isinstance(observation_space, spaces.Dict):
assert isinstance(
obs,
dict), "The observation returned by `step()` must be a dictionary"
for key in observation_space.spaces.keys():
try:
_check_obs(obs[key], observation_space.spaces[key], "step")
except AssertionError as e:
raise AssertionError(f"Error while checking key={key}: " +
str(e))
else:
_check_obs(obs, observation_space, "step")
# We also allow int because the reward will be cast to float
assert isinstance(
reward,
(float, int,
np.float32)), "The reward returned by `step()` must be a float"
assert isinstance(done, bool), "The `done` signal must be a boolean"
assert isinstance(
info,
dict), "The `info` returned by `step()` must be a python dictionary"
if isinstance(env, gym.GoalEnv):
# For a GoalEnv, the keys are checked at reset
assert reward == env.compute_reward(obs["achieved_goal"],
obs["desired_goal"], info)
def _check_returned_values(env: gym.Env, observation_space: Space, action_space: Space):
"""Check the returned values by the env when calling :meth:`env.reset` or :meth:`env.step` methods.
Args:
env: The environment
observation_space: The environment's observation space
action_space: The environment's action space
"""
# because env inherits from gym.Env, we assume that `reset()` and `step()` methods exists
obs = env.reset()
if isinstance(observation_space, Dict):
assert isinstance(
obs, dict
), "The observation returned by `reset()` must be a dictionary"
for key in observation_space.spaces.keys():
try:
_check_obs(obs[key], observation_space.spaces[key], "reset")
except AssertionError as e:
raise AssertionError(f"Error while checking key={key}: " + str(e))
else:
_check_obs(obs, observation_space, "reset")
# Sample a random action
action = action_space.sample()
data = env.step(action)
assert (
len(data) == 4
), "The `step()` method must return four values: obs, reward, done, info"
# Unpack
obs, reward, done, info = data
if isinstance(observation_space, Dict):
assert isinstance(
obs, dict
), "The observation returned by `step()` must be a dictionary"
for key in observation_space.spaces.keys():
try:
_check_obs(obs[key], observation_space.spaces[key], "step")
except AssertionError as e:
raise AssertionError(f"Error while checking key={key}: " + str(e))
else:
_check_obs(obs, observation_space, "step")
# We also allow int because the reward will be cast to float
assert isinstance(
reward, (float, int, np.float32)
), "The reward returned by `step()` must be a float"
assert isinstance(done, bool), "The `done` signal must be a boolean"
assert isinstance(
info, dict
), "The `info` returned by `step()` must be a python dictionary"
def get_obs_batch(batch_size: int, obs_space: Space,
num_tasks: int) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Sample a batch of (multi-task) observations and task indices. Note that `obs_space`
must be one-dimensional.
"""
obs_shape = obs_space.sample().shape
assert len(obs_shape) == 1
obs_len = obs_shape[0]
obs_list = []
for i in range(batch_size):
ob = torch.Tensor(obs_space.sample())
task_vector = one_hot_tensor(num_tasks)
obs_list.append(torch.cat([ob, task_vector]))
obs = torch.stack(obs_list)
nonzero_pos = obs[:, obs_len:].nonzero()
assert nonzero_pos[:, 0].tolist() == list(range(batch_size))
task_indices = nonzero_pos[:, 1]
return obs, task_indices
def check_run(env: gym.Env, action_space: spaces.Space):
"""Check normally running process of webotenv."""
num_env = 3
time_steps = 100
for _ in range(num_env):
env.reset()
for j in range(time_steps):
action = action_space.sample()
_, _, done, _ = env.step(action)
if done is True:
assert j+1 == env.steps_in_run, \
"The value of time steps is correct when 'done'"
break
if j == time_steps - 1:
assert env.steps_in_run == time_steps, \
"The number time steps are correct after steps > 1"
def check_reset_step(env: gym.Env, observation_space: spaces.Space,
action_space: spaces.Space):
""" Check reset() and step() function."""
obs_pre = env.reset()
_check_obs(obs_pre, observation_space, 'reset')
obs_current = env.reset()
assert (obs_pre[1:8] != obs_current[1:8]).any(), \
"The infos of the observation must differ after reset the webot env."
assert (obs_pre[10:] != obs_current[10:]).any(), \
"The infos of the lidar data must differ after reset the webot env."
for _ in range(3):
action = action_space.sample()
obs_next, _, _, _ = env.step(action)
_check_obs(obs_next, observation_space, 'step')
assert (obs_next[0:3] != obs_current[0:3]).any(), \
"The information of observation must be updated after the first action"
assert (obs_next[6:9] != obs_current[6:9]).any(), \
"The information of observation must be updated after the first action"
assert (obs_next[10:] != obs_current[10:]).any(), \
"The information of lidar data must be updated after the first action"