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import time
import elements
import numpy as np
class Wrapper:
def __init__(self, env):
self.env = env
def __len__(self):
return len(self.env)
def __bool__(self):
return bool(self.env)
def __getattr__(self, name):
if name.startswith('__'):
raise AttributeError(name)
try:
return getattr(self.env, name)
except AttributeError:
raise ValueError(name)
class TimeLimit(Wrapper):
def __init__(self, env, duration, reset=True):
super().__init__(env)
self._duration = duration
self._reset = reset
self._step = 0
self._done = False
def step(self, action):
if action['reset'] or self._done:
self._step = 0
self._done = False
if self._reset:
action.update(reset=True)
return self.env.step(action)
else:
action.update(reset=False)
obs = self.env.step(action)
obs['is_first'] = True
return obs
self._step += 1
obs = self.env.step(action)
if self._duration and self._step >= self._duration:
obs['is_last'] = True
self._done = obs['is_last']
return obs
class ActionRepeat(Wrapper):
def __init__(self, env, repeat):
super().__init__(env)
self._repeat = repeat
def step(self, action):
if action['reset']:
return self.env.step(action)
reward = 0.0
for _ in range(self._repeat):
obs = self.env.step(action)
reward += obs['reward']
if obs['is_last'] or obs['is_terminal']:
break
obs['reward'] = np.float32(reward)
return obs
class ClipAction(Wrapper):
def __init__(self, env, key='action', low=-1, high=1):
super().__init__(env)
self._key = key
self._low = low
self._high = high
def step(self, action):
clipped = np.clip(action[self._key], self._low, self._high)
return self.env.step({**action, self._key: clipped})
class NormalizeAction(Wrapper):
def __init__(self, env, key='action'):
super().__init__(env)
self._key = key
self._space = env.act_space[key]
self._mask = np.isfinite(self._space.low) & np.isfinite(self._space.high)
self._low = np.where(self._mask, self._space.low, -1)
self._high = np.where(self._mask, self._space.high, 1)
@functools.cached_property
def act_space(self):
low = np.where(self._mask, -np.ones_like(self._low), self._low)
high = np.where(self._mask, np.ones_like(self._low), self._high)
space = elements.Space(np.float32, self._space.shape, low, high)
return {**self.env.act_space, self._key: space}
def step(self, action):
orig = (action[self._key] + 1) / 2 * (self._high - self._low) + self._low
orig = np.where(self._mask, orig, action[self._key])
return self.env.step({**action, self._key: orig})
# class ExpandScalars(Wrapper):
#
# def __init__(self, env):
# super().__init__(env)
# self._obs_expanded = []
# self._obs_space = {}
# for key, space in self.env.obs_space.items():
# if space.shape == () and key != 'reward' and not space.discrete:
# space = elements.Space(space.dtype, (1,), space.low, space.high)
# self._obs_expanded.append(key)
# self._obs_space[key] = space
# self._act_expanded = []
# self._act_space = {}
# for key, space in self.env.act_space.items():
# if space.shape == () and not space.discrete:
# space = elements.Space(space.dtype, (1,), space.low, space.high)
# self._act_expanded.append(key)
# self._act_space[key] = space
#
# @functools.cached_property
# def obs_space(self):
# return self._obs_space
#
# @functools.cached_property
# def act_space(self):
# return self._act_space
#
# def step(self, action):
# action = {
# key: np.squeeze(value, 0) if key in self._act_expanded else value
# for key, value in action.items()}
# obs = self.env.step(action)
# obs = {
# key: np.expand_dims(value, 0) if key in self._obs_expanded else value
# for key, value in obs.items()}
# return obs
#
#
# class FlattenTwoDimObs(Wrapper):
#
# def __init__(self, env):
# super().__init__(env)
# self._keys = []
# self._obs_space = {}
# for key, space in self.env.obs_space.items():
# if len(space.shape) == 2:
# space = elements.Space(
# space.dtype,
# (int(np.prod(space.shape)),),
# space.low.flatten(),
# space.high.flatten())
# self._keys.append(key)
# self._obs_space[key] = space
#
# @functools.cached_property
# def obs_space(self):
# return self._obs_space
#
# def step(self, action):
# obs = self.env.step(action).copy()
# for key in self._keys:
# obs[key] = obs[key].flatten()
# return obs
#
#
# class FlattenTwoDimActions(Wrapper):
#
# def __init__(self, env):
# super().__init__(env)
# self._origs = {}
# self._act_space = {}
# for key, space in self.env.act_space.items():
# if len(space.shape) == 2:
# space = elements.Space(
# space.dtype,
# (int(np.prod(space.shape)),),
# space.low.flatten(),
# space.high.flatten())
# self._origs[key] = space.shape
# self._act_space[key] = space
#
# @functools.cached_property
# def act_space(self):
# return self._act_space
#
# def step(self, action):
# action = action.copy()
# for key, shape in self._origs.items():
# action[key] = action[key].reshape(shape)
# return self.env.step(action)
class UnifyDtypes(Wrapper):
def __init__(self, env):
super().__init__(env)
self._obs_space, _, self._obs_outer = self._convert(env.obs_space)
self._act_space, self._act_inner, _ = self._convert(env.act_space)
@property
def obs_space(self):
return self._obs_space
@property
def act_space(self):
return self._act_space
def step(self, action):
action = action.copy()
for key, dtype in self._act_inner.items():
action[key] = np.asarray(action[key], dtype)
obs = self.env.step(action)
for key, dtype in self._obs_outer.items():
obs[key] = np.asarray(obs[key], dtype)
return obs
def _convert(self, spaces):
results, befores, afters = {}, {}, {}
for key, space in spaces.items():
before = after = space.dtype
if np.issubdtype(before, np.floating):
after = np.float32
elif np.issubdtype(before, np.uint8):
after = np.uint8
elif np.issubdtype(before, np.integer):
after = np.int32
befores[key] = before
afters[key] = after
results[key] = elements.Space(after, space.shape, space.low, space.high)
return results, befores, afters
class CheckSpaces(Wrapper):
def __init__(self, env):
assert not (env.obs_space.keys() & env.act_space.keys()), (
env.obs_space.keys(), env.act_space.keys())
super().__init__(env)
def step(self, action):
for key, value in action.items():
self._check(value, self.env.act_space[key], key)
obs = self.env.step(action)
for key, value in obs.items():
self._check(value, self.env.obs_space[key], key)
return obs
def _check(self, value, space, key):
if not isinstance(value, (
np.ndarray, np.generic, list, tuple, int, float, bool)):
raise TypeError(f'Invalid type {type(value)} for key {key}.')
if value in space:
return
dtype = np.array(value).dtype
shape = np.array(value).shape
lowest, highest = np.min(value), np.max(value)
raise ValueError(
f"Value for '{key}' with dtype {dtype}, shape {shape}, "
f"lowest {lowest}, highest {highest} is not in {space}.")
class DiscretizeAction(Wrapper):
def __init__(self, env, key='action', bins=5):
super().__init__(env)
self._dims = np.squeeze(env.act_space[key].shape, 0).item()
self._values = np.linspace(-1, 1, bins)
self._key = key
@functools.cached_property
def act_space(self):
space = elements.Space(np.int32, self._dims, 0, len(self._values))
return {**self.env.act_space, self._key: space}
def step(self, action):
continuous = np.take(self._values, action[self._key])
return self.env.step({**action, self._key: continuous})
class ResizeImage(Wrapper):
def __init__(self, env, size=(64, 64)):
super().__init__(env)
self._size = size
self._keys = [
k for k, v in env.obs_space.items()
if len(v.shape) > 1 and v.shape[:2] != size]
print(f'Resizing keys {",".join(self._keys)} to {self._size}.')
if self._keys:
from PIL import Image
self._Image = Image
@functools.cached_property
def obs_space(self):
spaces = self.env.obs_space
for key in self._keys:
shape = self._size + spaces[key].shape[2:]
spaces[key] = elements.Space(np.uint8, shape)
return spaces
def step(self, action):
obs = self.env.step(action)
for key in self._keys:
obs[key] = self._resize(obs[key])
return obs
def _resize(self, image):
image = self._Image.fromarray(image)
image = image.resize(self._size, self._Image.NEAREST)
image = np.array(image)
return image
# class RenderImage(Wrapper):
#
# def __init__(self, env, key='image'):
# super().__init__(env)
# self._key = key
# self._shape = self.env.render().shape
#
# @functools.cached_property
# def obs_space(self):
# spaces = self.env.obs_space
# spaces[self._key] = elements.Space(np.uint8, self._shape)
# return spaces
#
# def step(self, action):
# obs = self.env.step(action)
# obs[self._key] = self.env.render()
# return obs
class BackwardReturn(Wrapper):
def __init__(self, env, horizon):
super().__init__(env)
self._discount = 1 - 1 / horizon
self._bwreturn = 0.0
@functools.cached_property
def obs_space(self):
return {
**self.env.obs_space,
'bwreturn': elements.Space(np.float32),
}
def step(self, action):
obs = self.env.step(action)
self._bwreturn *= (1 - obs['is_first']) * self._discount
self._bwreturn += obs['reward']
obs['bwreturn'] = np.float32(self._bwreturn)
return obs
class AddObs(Wrapper):
def __init__(self, env, key, value, space):
super().__init__(env)
self._key = key
self._value = value
self._space = space
@functools.cached_property
def obs_space(self):
return {
**self.env.obs_space,
self._key: self._space,
}
def step(self, action):
obs = self.env.step(action)
obs[self._key] = self._value
return obs
class RestartOnException(Wrapper):
def __init__(
self, ctor, exceptions=(Exception,), window=300, maxfails=2, wait=20):
if not isinstance(exceptions, (tuple, list)):
exceptions = [exceptions]
self._ctor = ctor
self._exceptions = tuple(exceptions)
self._window = window
self._maxfails = maxfails
self._wait = wait
self._last = time.time()
self._fails = 0
super().__init__(self._ctor())
def step(self, action):
try:
return self.env.step(action)
except self._exceptions as e:
if time.time() > self._last + self._window:
self._last = time.time()
self._fails = 1
else:
self._fails += 1
if self._fails > self._maxfails:
raise RuntimeError('The env crashed too many times.')
message = f'Restarting env after crash with {type(e).__name__}: {e}'
print(message, flush=True)
time.sleep(self._wait)
self.env = self._ctor()
action['reset'] = np.ones_like(action['reset'])
return self.env.step(action)
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