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env_wrappers.py
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env_wrappers.py
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from collections import OrderedDict
import copy
from logging import getLogger
import time
from collections import deque
import gym
import numpy as np
import cv2
from gym.wrappers import Monitor
from gym.wrappers.monitoring.stats_recorder import StatsRecorder
from pfrl.wrappers.atari_wrappers import LazyFrames
cv2.ocl.setUseOpenCL(False)
logger = getLogger(__name__)
class ResetTrimInfoWrapper(gym.Wrapper):
"""Take first return value.
minerl's `env.reset()` returns tuple of `(obs, info)`
but existing agent implementations expect `reset()` returns `obs` only.
"""
def reset(self, **kwargs):
obs, info = self.env.reset(**kwargs)
return obs
class ContinuingTimeLimitMonitor(Monitor):
"""`Monitor` with ChainerRL's `ContinuingTimeLimit` support.
Because of the original implementation's design,
explicit `close()` is needed to save the last episode.
Do not forget to call `close()` at the last line of your script.
For details, see
https://github.com/openai/gym/blob/master/gym/wrappers/monitor.py
"""
def _start(self, directory, video_callable=None, force=False, resume=False,
write_upon_reset=False, uid=None, mode=None):
if self.env_semantics_autoreset:
raise gym.error.Error(
"Detect 'semantics.autoreset=True' in `env.metadata`, "
"which means the env comes from deprecated OpenAI Universe.")
ret = super()._start(directory=directory,
video_callable=video_callable, force=force,
resume=resume, write_upon_reset=write_upon_reset,
uid=uid, mode=mode)
if self.env.spec is None:
env_id = '(unknown)'
else:
env_id = self.env.spec.id
self.stats_recorder = _ContinuingTimeLimitStatsRecorder(
directory,
'{}.episode_batch.{}'.format(self.file_prefix, self.file_infix),
autoreset=False, env_id=env_id)
return ret
class _ContinuingTimeLimitStatsRecorder(StatsRecorder):
"""`StatsRecorder` with ChainerRL's `ContinuingTimeLimit` support.
For details, see
https://github.com/openai/gym/blob/master/gym/wrappers/monitoring/stats_recorder.py
"""
def __init__(self, directory, file_prefix, autoreset=False, env_id=None):
super().__init__(directory, file_prefix,
autoreset=autoreset, env_id=env_id)
self._save_completed = True
def before_reset(self):
assert not self.closed
if self.done is not None and not self.done and self.steps > 0:
logger.debug('Tried to reset env which is not done. '
'StatsRecorder completes the last episode.')
self.save_complete()
self.done = False
if self.initial_reset_timestamp is None:
self.initial_reset_timestamp = time.time()
def after_step(self, observation, reward, done, info):
self._save_completed = False
return super().after_step(observation, reward, done, info)
def save_complete(self):
if not self._save_completed:
super().save_complete()
self._save_completed = True
def close(self):
self.save_complete()
super().close()
class FrameSkip(gym.Wrapper):
"""Return every `skip`-th frame and repeat given action during skip.
Note that this wrapper does not "maximize" over the skipped frames.
"""
def __init__(self, env, skip=4):
super().__init__(env)
self._skip = skip
def step(self, action):
total_reward = 0.0
for _ in range(self._skip):
obs, reward, done, info = self.env.step(action)
total_reward += reward
if done:
break
return obs, total_reward, done, info
class FrameStack(gym.Wrapper):
def __init__(self, env, k, channel_order='hwc', use_tuple=False):
"""Stack k last frames.
Returns lazy array, which is much more memory efficient.
"""
gym.Wrapper.__init__(self, env)
self.k = k
self.observations = deque([], maxlen=k)
self.stack_axis = {'hwc': 2, 'chw': 0}[channel_order]
self.use_tuple = use_tuple
if self.use_tuple:
pov_space = env.observation_space[0]
inv_space = env.observation_space[1]
else:
pov_space = env.observation_space
low_pov = np.repeat(pov_space.low, k, axis=self.stack_axis)
high_pov = np.repeat(pov_space.high, k, axis=self.stack_axis)
pov_space = gym.spaces.Box(low=low_pov, high=high_pov, dtype=pov_space.dtype)
if self.use_tuple:
low_inv = np.repeat(inv_space.low, k, axis=0)
high_inv = np.repeat(inv_space.high, k, axis=0)
inv_space = gym.spaces.Box(low=low_inv, high=high_inv, dtype=inv_space.dtype)
self.observation_space = gym.spaces.Tuple(
(pov_space, inv_space))
else:
self.observation_space = pov_space
def reset(self):
ob = self.env.reset()
for _ in range(self.k):
self.observations.append(ob)
return self._get_ob()
def step(self, action):
ob, reward, done, info = self.env.step(action)
self.observations.append(ob)
return self._get_ob(), reward, done, info
def _get_ob(self):
assert len(self.observations) == self.k
if self.use_tuple:
frames = [x[0] for x in self.observations]
inventory = [x[1] for x in self.observations]
return (LazyFrames(list(frames), stack_axis=self.stack_axis),
LazyFrames(list(inventory), stack_axis=0))
else:
return LazyFrames(list(self.observations), stack_axis=self.stack_axis)
class ObtainPoVWrapper(gym.ObservationWrapper):
"""Obtain 'pov' value (current game display) of the original observation."""
def __init__(self, env):
super().__init__(env)
self.observation_space = self.env.observation_space.spaces['pov']
def observation(self, observation):
return observation['pov']
class PoVWithCompassAngleWrapper(gym.ObservationWrapper):
"""Take 'pov' value (current game display) and concatenate compass angle information with it, as a new channel of image;
resulting image has RGB+compass (or K+compass for gray-scaled image) channels.
"""
def __init__(self, env):
super().__init__(env)
self._compass_angle_scale = 180 / 255 # NOTE: `ScaledFloatFrame` will scale the pixel values with 255.0 later
pov_space = self.env.observation_space.spaces['pov']
compass_angle_space = self.env.observation_space.spaces['compassAngle']
low = self.observation({'pov': pov_space.low, 'compassAngle': compass_angle_space.low})
high = self.observation({'pov': pov_space.high, 'compassAngle': compass_angle_space.high})
self.observation_space = gym.spaces.Box(low=low, high=high)
def observation(self, observation):
pov = observation['pov']
compass_scaled = observation['compassAngle'] / self._compass_angle_scale
compass_channel = np.ones(shape=list(pov.shape[:-1]) + [1], dtype=pov.dtype) * compass_scaled
return np.concatenate([pov, compass_channel], axis=-1)
class UnifiedObservationWrapper(gym.ObservationWrapper):
"""Take 'pov', 'compassAngle', 'inventory' and concatenate with scaling.
Each element of 'inventory' is converted to a square whose side length is region_size.
The color of each square is correlated to the reciprocal of (the number of the corresponding item + 1).
"""
def __init__(self, env, region_size=8):
super().__init__(env)
self._compass_angle_scale = 180 / 255 # NOTE: `ScaledFloatFrame` will scale the pixel values with 255.0 later
self.region_size = region_size
pov_space = self.env.observation_space.spaces['pov']
low_dict = {'pov': pov_space.low}
high_dict = {'pov': pov_space.high}
if 'compassAngle' in self.env.observation_space.spaces:
compass_angle_space = self.env.observation_space.spaces['compassAngle']
low_dict['compassAngle'] = compass_angle_space.low
high_dict['compassAngle'] = compass_angle_space.high
if 'inventory' in self.env.observation_space.spaces:
inventory_space = self.env.observation_space.spaces['inventory']
low_dict['inventory'] = {}
high_dict['inventory'] = {}
for key in inventory_space.spaces.keys():
low_dict['inventory'][key] = inventory_space.spaces[key].low
high_dict['inventory'][key] = inventory_space.spaces[key].high
low = self.observation(low_dict)
high = self.observation(high_dict)
self.observation_space = gym.spaces.Box(low=low, high=high)
def observation(self, observation):
obs = observation['pov']
pov_dtype = obs.dtype
if 'compassAngle' in observation:
compass_scaled = observation['compassAngle'] / self._compass_angle_scale
compass_channel = np.ones(shape=list(obs.shape[:-1]) + [1], dtype=pov_dtype) * compass_scaled
obs = np.concatenate([obs, compass_channel], axis=-1)
if 'inventory' in observation:
assert len(obs.shape[:-1]) == 2
region_max_height = obs.shape[0]
region_max_width = obs.shape[1]
rs = self.region_size
if min(region_max_height, region_max_width) < rs:
raise ValueError("'region_size' is too large.")
num_element_width = region_max_width // rs
inventory_channel = np.zeros(shape=list(obs.shape[:-1]) + [1], dtype=pov_dtype)
for idx, key in enumerate(observation['inventory']):
item_scaled = np.clip(255 - 255 / (observation['inventory'][key] + 1), # Inversed
0, 255)
item_channel = np.ones(shape=[rs, rs, 1], dtype=pov_dtype) * item_scaled
width_low = (idx % num_element_width) * rs
height_low = (idx // num_element_width) * rs
if height_low + rs > region_max_height:
raise ValueError("Too many elements on 'inventory'. Please decrease 'region_size' of each component")
inventory_channel[height_low:(height_low + rs), width_low:(width_low + rs), :] = item_channel
obs = np.concatenate([obs, inventory_channel], axis=-1)
return obs
class FullObservationSpaceWrapper(gym.ObservationWrapper):
"""Returns as observation a tuple with the frames and a list of
compassAngle and inventory items.
compassAngle is scaled to be in the interval [-1, 1] and inventory items
are scaled to be in the interval [0, 1]
"""
def __init__(self, env):
super().__init__(env)
pov_space = self.env.observation_space.spaces['pov']
low_dict = {'pov': pov_space.low, 'inventory': {}}
high_dict = {'pov': pov_space.high, 'inventory': {}}
for obs_name in self.env.observation_space.spaces['inventory'].spaces.keys():
obs_space = self.env.observation_space.spaces['inventory'].spaces[obs_name]
low_dict['inventory'][obs_name] = obs_space.low
high_dict['inventory'][obs_name] = obs_space.high
if 'compassAngle' in self.env.observation_space.spaces:
compass_angle_space = self.env.observation_space.spaces['compassAngle']
low_dict['compassAngle'] = compass_angle_space.low
high_dict['compassAngle'] = compass_angle_space.high
low = self.observation(low_dict)
high = self.observation(high_dict)
pov_space = gym.spaces.Box(low=low[0], high=high[0])
inventory_space = gym.spaces.Box(low=low[1], high=high[1])
self.observation_space = gym.spaces.Tuple((pov_space, inventory_space))
def observation(self, observation):
frame = observation['pov']
inventory = []
if 'compassAngle' in observation:
compass_scaled = observation['compassAngle'] / 180
inventory.append(compass_scaled)
for obs_name in observation['inventory'].keys():
inventory.append(observation['inventory'][obs_name] / 2304)
inventory = np.array(inventory)
return (frame, inventory)
class MoveAxisWrapper(gym.ObservationWrapper):
"""Move axes of observation ndarrays."""
def __init__(self, env, source, destination, use_tuple=False):
if use_tuple:
assert isinstance(env.observation_space[0], gym.spaces.Box)
else:
assert isinstance(env.observation_space, gym.spaces.Box)
super().__init__(env)
self.source = source
self.destination = destination
self.use_tuple = use_tuple
if self.use_tuple:
low = self.observation(
tuple([space.low for space in self.observation_space]))
high = self.observation(
tuple([space.high for space in self.observation_space]))
dtype = self.observation_space[0].dtype
pov_space = gym.spaces.Box(low=low[0], high=high[0], dtype=dtype)
inventory_space = self.observation_space[1]
self.observation_space = gym.spaces.Tuple(
(pov_space, inventory_space))
else:
low = self.observation(self.observation_space.low)
high = self.observation(self.observation_space.high)
dtype = self.observation_space.dtype
self.observation_space = gym.spaces.Box(
low=low, high=high, dtype=dtype)
def observation(self, observation):
if self.use_tuple:
new_observation = list(observation)
new_observation[0] = np.moveaxis(
observation[0], self.source, self.destination)
return tuple(new_observation)
else:
return np.moveaxis(observation, self.source, self.destination)
class GrayScaleWrapper(gym.ObservationWrapper):
def __init__(self, env, dict_space_key=None):
super().__init__(env)
self._key = dict_space_key
if self._key is None:
original_space = self.observation_space
else:
original_space = self.observation_space.spaces[self._key]
height, width = original_space.shape[0], original_space.shape[1]
# sanity checks
ideal_image_space = gym.spaces.Box(low=0, high=255, shape=(height, width, 3), dtype=np.uint8)
if original_space != ideal_image_space:
raise ValueError('Image space should be {}, but given {}.'.format(ideal_image_space, original_space))
if original_space.dtype != np.uint8:
raise ValueError('Image should `np.uint8` typed, but given {}.'.format(original_space.dtype))
height, width = original_space.shape[0], original_space.shape[1]
new_space = gym.spaces.Box(low=0, high=255, shape=(height, width, 1), dtype=np.uint8)
if self._key is None:
self.observation_space = new_space
else:
new_space_dict = copy.deepcopy(self.observation_space)
new_space_dict.spaces[self._key] = new_space
self.observation_space = new_space_dict
def observation(self, obs):
if self._key is None:
frame = obs
else:
frame = obs[self._key]
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
frame = np.expand_dims(frame, -1)
if self._key is None:
obs = frame
else:
obs[self._key] = frame
return obs
class SerialDiscreteActionWrapper(gym.ActionWrapper):
"""Convert MineRL env's `Dict` action space as a serial discrete action space.
The term "serial" means that this wrapper can only push one key at each step.
"attack" action will be alwarys triggered.
Parameters
----------
env
Wrapping gym environment.
always_keys
List of action keys, which should be always pressed throughout interaction with environment.
If specified, the "noop" action is also affected.
reverse_keys
List of action keys, which should be always pressed but can be turn off via action.
If specified, the "noop" action is also affected.
exclude_keys
List of action keys, which should be ignored for discretizing action space.
exclude_noop
The "noop" will be excluded from discrete action list.
num_camera_discretize
Number of discretization of yaw control (must be odd).
allow_pitch
If specified, this wrapper appends commands to control pitch.
max_camera_range
Maximum value of yaw control.
"""
BINARY_KEYS = ['forward', 'back', 'left', 'right', 'jump', 'sneak', 'sprint', 'attack']
def __init__(self, env, always_keys=None, reverse_keys=None, exclude_keys=None, exclude_noop=False,
num_camera_discretize=3, allow_pitch=False,
max_camera_range=10):
super().__init__(env)
self.always_keys = [] if always_keys is None else always_keys
self.reverse_keys = [] if reverse_keys is None else reverse_keys
self.exclude_keys = [] if exclude_keys is None else exclude_keys
if len(set(self.always_keys) | set(self.reverse_keys) | set(self.exclude_keys)) != \
len(self.always_keys) + len(self.reverse_keys) + len(self.exclude_keys):
raise ValueError('always_keys ({}) or reverse_keys ({}) or exclude_keys ({}) intersect each other.'.format(
self.always_keys, self.reverse_keys, self.exclude_keys))
self.exclude_noop = exclude_noop
self.wrapping_action_space = self.env.action_space
self.num_camera_discretize = num_camera_discretize
self._noop_template = OrderedDict([
('forward', 0),
('back', 0),
('left', 0),
('right', 0),
('jump', 0),
('sneak', 0),
('sprint', 0),
('attack', 0),
('camera', np.zeros((2, ), dtype=np.float32)),
# 'none', 'dirt' (Obtain*:)+ 'stone', 'cobblestone', 'crafting_table', 'furnace', 'torch'
('place', 0),
# (Obtain* tasks only) 'none', 'wooden_axe', 'wooden_pickaxe', 'stone_axe', 'stone_pickaxe', 'iron_axe', 'iron_pickaxe'
('equip', 0),
# (Obtain* tasks only) 'none', 'torch', 'stick', 'planks', 'crafting_table'
('craft', 0),
# (Obtain* tasks only) 'none', 'wooden_axe', 'wooden_pickaxe', 'stone_axe', 'stone_pickaxe', 'iron_axe', 'iron_pickaxe', 'furnace'
('nearbyCraft', 0),
# (Obtain* tasks only) 'none', 'iron_ingot', 'coal'
('nearbySmelt', 0),
])
for key, space in self.wrapping_action_space.spaces.items():
if key not in self._noop_template:
raise ValueError('Unknown action name: {}'.format(key))
# get noop
self.noop = copy.deepcopy(self._noop_template)
for key in self._noop_template:
if key not in self.wrapping_action_space.spaces:
del self.noop[key]
# check&set always_keys
for key in self.always_keys:
if key not in self.BINARY_KEYS:
raise ValueError('{} is not allowed for `always_keys`.'.format(key))
self.noop[key] = 1
logger.info('always pressing keys: {}'.format(self.always_keys))
# check&set reverse_keys
for key in self.reverse_keys:
if key not in self.BINARY_KEYS:
raise ValueError('{} is not allowed for `reverse_keys`.'.format(key))
self.noop[key] = 1
logger.info('reversed pressing keys: {}'.format(self.reverse_keys))
# check exclude_keys
for key in self.exclude_keys:
if key not in self.noop:
raise ValueError('unknown exclude_keys: {}'.format(key))
logger.info('always ignored keys: {}'.format(self.exclude_keys))
# get each discrete action
self._actions = [self.noop]
for key in self.noop:
if key in self.always_keys or key in self.exclude_keys:
continue
if key in self.BINARY_KEYS:
# action candidate : {1} (0 is ignored because it is for noop), or {0} when `reverse_keys`.
op = copy.deepcopy(self.noop)
if key in self.reverse_keys:
op[key] = 0
else:
op[key] = 1
self._actions.append(op)
elif key == 'camera':
# action candidate : {[0, -max_camera_range], [0, -max_camera_range + delta_range], ..., [0, max_camera_range]}
# ([0, 0] is excluded)
delta_range = max_camera_range * 2 / (self.num_camera_discretize - 1)
if self.num_camera_discretize % 2 == 0:
raise ValueError('Number of camera discretization must be odd.')
for i in range(self.num_camera_discretize):
op = copy.deepcopy(self.noop)
if i < self.num_camera_discretize // 2:
op[key] = np.array([0, -max_camera_range + delta_range * i], dtype=np.float32)
elif i > self.num_camera_discretize // 2:
op[key] = np.array([0, -max_camera_range + delta_range * (i - 1)], dtype=np.float32)
else:
continue
self._actions.append(op)
if allow_pitch:
for i in range(self.num_camera_discretize):
op = copy.deepcopy(self.noop)
if i < self.num_camera_discretize // 2:
op[key] = np.array([-max_camera_range + delta_range * i, 0], dtype=np.float32)
elif i > self.num_camera_discretize // 2:
op[key] = np.array([-max_camera_range + delta_range * (i - 1), 0], dtype=np.float32)
else:
continue
self._actions.append(op)
elif key in {'place', 'equip', 'craft', 'nearbyCraft', 'nearbySmelt'}:
# action candidate : {1, 2, ..., len(space)-1} (0 is ignored because it is for noop)
for a in range(1, self.wrapping_action_space.spaces[key].n):
op = copy.deepcopy(self.noop)
op[key] = a
self._actions.append(op)
if self.exclude_noop:
del self._actions[0]
n = len(self._actions)
self.action_space = gym.spaces.Discrete(n)
logger.info('{} is converted to {}.'.format(self.wrapping_action_space, self.action_space))
def action(self, action):
if not self.action_space.contains(action):
raise ValueError('action {} is invalid for {}'.format(action, self.action_space))
original_space_action = self._actions[action]
logger.debug('discrete action {} -> original action {}'.format(action, original_space_action))
return original_space_action
class CombineActionWrapper(gym.ActionWrapper):
"""Combine MineRL env's "exclusive" actions.
"exclusive" actions will be combined as:
- "forward", "back" -> noop/forward/back (Discrete(3))
- "left", "right" -> noop/left/right (Discrete(3))
- "sneak", "sprint" -> noop/sneak/sprint (Discrete(3))
- "attack", "place", "equip", "craft", "nearbyCraft", "nearbySmelt"
-> noop/attack/place/equip/craft/nearbyCraft/nearbySmelt (Discrete(n))
The combined action's names will be concatenation of originals, i.e.,
"forward_back", "left_right", "snaek_sprint", "attack_place_equip_craft_nearbyCraft_nearbySmelt".
"""
def __init__(self, env):
super().__init__(env)
self.wrapping_action_space = self.env.action_space
def combine_exclusive_actions(keys):
"""
Dict({'forward': Discrete(2), 'back': Discrete(2)})
=>
new_actions: [{'forward':0, 'back':0}, {'forward':1, 'back':0}, {'forward':0, 'back':1}]
"""
new_key = '_'.join(keys)
valid_action_keys = [k for k in keys if k in self.wrapping_action_space.spaces]
noop = {a: 0 for a in valid_action_keys}
new_actions = [noop]
for key in valid_action_keys:
space = self.wrapping_action_space.spaces[key]
for i in range(1, space.n):
op = copy.deepcopy(noop)
op[key] = i
new_actions.append(op)
return new_key, new_actions
self._maps = {}
for keys in (
('forward', 'back'), ('left', 'right'), ('sneak', 'sprint'),
('attack', 'place', 'equip', 'craft', 'nearbyCraft', 'nearbySmelt')):
new_key, new_actions = combine_exclusive_actions(keys)
self._maps[new_key] = new_actions
self.noop = OrderedDict([
('forward_back', 0),
('left_right', 0),
('jump', 0),
('sneak_sprint', 0),
('camera', np.zeros((2, ), dtype=np.float32)),
('attack_place_equip_craft_nearbyCraft_nearbySmelt', 0),
])
self.action_space = gym.spaces.Dict({
'forward_back':
gym.spaces.Discrete(len(self._maps['forward_back'])),
'left_right':
gym.spaces.Discrete(len(self._maps['left_right'])),
'jump':
self.wrapping_action_space.spaces['jump'],
'sneak_sprint':
gym.spaces.Discrete(len(self._maps['sneak_sprint'])),
'camera':
self.wrapping_action_space.spaces['camera'],
'attack_place_equip_craft_nearbyCraft_nearbySmelt':
gym.spaces.Discrete(len(self._maps['attack_place_equip_craft_nearbyCraft_nearbySmelt']))
})
logger.info('{} is converted to {}.'.format(self.wrapping_action_space, self.action_space))
for k, v in self._maps.items():
logger.info('{} -> {}'.format(k, v))
def action(self, action):
if not self.action_space.contains(action):
raise ValueError('action {} is invalid for {}'.format(action, self.action_space))
original_space_action = OrderedDict()
for k, v in action.items():
if k in self._maps:
a = self._maps[k][v]
original_space_action.update(a)
else:
original_space_action[k] = v
logger.debug('action {} -> original action {}'.format(action, original_space_action))
return original_space_action
class SerialDiscreteCombineActionWrapper(gym.ActionWrapper):
def __init__(self, env):
super().__init__(env)
self.wrapping_action_space = self.env.action_space
self.noop = OrderedDict([
('forward_back', 0),
('left_right', 0),
('jump', 0),
('sneak_sprint', 0),
('camera', np.zeros((2, ), dtype=np.float32)),
('attack_place_equip_craft_nearbyCraft_nearbySmelt', 0),
])
# get each discrete action
self._actions = [self.noop]
for key in self.noop:
if key == 'camera':
# action candidate : {[0, -10], [0, 10]}
op = copy.deepcopy(self.noop)
op[key] = np.array([0, -10], dtype=np.float32)
self._actions.append(op)
op = copy.deepcopy(self.noop)
op[key] = np.array([0, 10], dtype=np.float32)
self._actions.append(op)
else:
for a in range(1, self.wrapping_action_space.spaces[key].n):
op = copy.deepcopy(self.noop)
op[key] = a
self._actions.append(op)
n = len(self._actions)
self.action_space = gym.spaces.Discrete(n)
logger.info('{} is converted to {}.'.format(self.wrapping_action_space, self.action_space))
def action(self, action):
if not self.action_space.contains(action):
raise ValueError('action {} is invalid for {}'.format(action, self.action_space))
original_space_action = self._actions[action]
logger.debug('discrete action {} -> original action {}'.format(action, original_space_action))
return original_space_action
class NormalizedContinuousActionWrapper(gym.ActionWrapper):
"""Convert MineRL env's `Dict` action space as a continuous action space.
Parameters
----------
env
Wrapping gym environment.
"""
BINARY_KEYS = ['forward', 'back', 'left', 'right', 'jump', 'sneak', 'sprint', 'attack']
def __init__(self, env, allow_pitch=False, max_camera_range=10):
super().__init__(env)
self.allow_pitch = allow_pitch
self.wrapping_action_space = self.env.action_space
self._noop_template = OrderedDict([
('forward', 0),
('back', 0),
('left', 0),
('right', 0),
('jump', 0),
('sneak', 0),
('sprint', 0),
('attack', 0),
('camera', np.zeros((2, ), dtype=np.float32)),
# 'none', 'dirt' (Obtain*:)+ 'stone', 'cobblestone', 'crafting_table', 'furnace', 'torch'
('place', 0),
# (Obtain* tasks only) 'none', 'wooden_axe', 'wooden_pickaxe', 'stone_axe', 'stone_pickaxe', 'iron_axe', 'iron_pickaxe'
('equip', 0),
# (Obtain* tasks only) 'none', 'torch', 'stick', 'planks', 'crafting_table'
('craft', 0),
# (Obtain* tasks only) 'none', 'wooden_axe', 'wooden_pickaxe', 'stone_axe', 'stone_pickaxe', 'iron_axe', 'iron_pickaxe', 'furnace'
('nearbyCraft', 0),
# (Obtain* tasks only) 'none', 'iron_ingot', 'coal'
('nearbySmelt', 0),
])
for key, space in self.wrapping_action_space.spaces.items():
if key not in self._noop_template:
raise ValueError('Unknown action name: {}'.format(key))
# get noop
self.noop = copy.deepcopy(self._noop_template)
for key in self._noop_template:
if key not in self.wrapping_action_space.spaces:
del self.noop[key]
value_means = []
value_ranges = []
self.is_binary = []
for key in self.noop:
if key in self.BINARY_KEYS:
value_means.append(0.5)
value_ranges.append(0.5)
self.is_binary.append(True)
elif key == 'camera':
value_means.append(0)
value_means.append(0)
self.is_binary.append(False)
value_ranges.append(max_camera_range)
value_ranges.append(max_camera_range)
self.is_binary.append(False)
elif key in {'place', 'craft', 'nearbyCraft', 'nearbySmelt'}:
# TODO: implementation
value_means.append(0)
value_ranges.append(0)
self.value_means = np.array(value_means, dtype=np.float32)
self.value_ranges = np.array(value_ranges, dtype=np.float32)
n = len(self.value_means)
self.action_space = gym.spaces.Box(low=-np.ones(n), high=np.ones(n), dtype=np.float32)
logger.info('{} is converted to {}.'.format(self.wrapping_action_space, self.action_space))
def _action(self, action):
original_action = copy.deepcopy(self.noop)
idx = 0
for key, is_binary in zip(self.noop, self.is_binary):
if key == 'camera':
orig_values = np.clip(action[idx:idx + 2], -1, 1)
values = (orig_values * self.value_ranges[idx:idx + 2]
+ self.value_means[idx:idx + 2])
if not self.allow_pitch:
values[0] = 0
original_action[key] = values
idx += 2
elif is_binary:
value = (action[idx] * self.value_ranges[idx]
+ self.value_means[idx])
if np.random.rand() < value:
original_action[key] = 1
else:
original_action[key] = 0
idx += 1
else:
# noop
# value = (action[idx] * self.value_ranges[idx]
# + self.value_means[idx])
# original_action[key] = value
idx += 1
return original_action
class MultiDimensionalSoftmaxActionWrapper(gym.ActionWrapper):
BINARY_KEYS = ['forward', 'back', 'left', 'right', 'jump', 'sneak', 'sprint', 'attack']
def __init__(self, env, allow_pitch=False, max_camera_range=10,
num_camera_discretize=7):
super().__init__(env)
self.allow_pitch = allow_pitch
self.max_camera_range = max_camera_range
self.num_camera_discretize = num_camera_discretize
self.wrapping_action_space = self.env.action_space
self.noop = OrderedDict([
('forward', 0),
('back', 0),
('left', 0),
('right', 0),
('jump', 0),
('sneak', 0),
('sprint', 0),
('attack', 0),
('camera', np.zeros((2, ), dtype=np.float32)),
# 'none', 'dirt' (Obtain*:)+ 'stone', 'cobblestone', 'crafting_table', 'furnace', 'torch'
('place', 0),
# (Obtain* tasks only) 'none', 'wooden_axe', 'wooden_pickaxe', 'stone_axe', 'stone_pickaxe', 'iron_axe', 'iron_pickaxe'
('equip', 0),
# (Obtain* tasks only) 'none', 'torch', 'stick', 'planks', 'crafting_table'
('craft', 0),
# (Obtain* tasks only) 'none', 'wooden_axe', 'wooden_pickaxe', 'stone_axe', 'stone_pickaxe', 'iron_axe', 'iron_pickaxe', 'furnace'
('nearbyCraft', 0),
# (Obtain* tasks only) 'none', 'iron_ingot', 'coal'
('nearbySmelt', 0),
])
for key, space in self.wrapping_action_space.spaces.items():
if key not in self.noop:
raise ValueError('Unknown action name: {}'.format(key))
_noop_template = copy.deepcopy(self.noop)
for key in _noop_template:
if key not in self.wrapping_action_space.spaces:
del self.noop[key]
# get each discrete action
self._actions = [self.noop]
num_actions = []
for key in self.noop:
if self.num_camera_discretize % 2 == 0:
raise ValueError('Number of camera discretization must be odd.')
if key == 'camera':
num_actions.append(self.num_camera_discretize)
num_actions.append(self.num_camera_discretize)
else:
num_actions.append(self.wrapping_action_space.spaces[key].n)
self.action_space = gym.spaces.Box(
low=np.zeros_like(num_actions),
high=(np.array(num_actions) - 1),
dtype=np.int32)
logger.info('{} is converted to {}.'.format(self.wrapping_action_space, self.action_space))
def action(self, action):
original_action = OrderedDict(copy.deepcopy(self.noop))
half_scale = (self.num_camera_discretize - 1) / 2
idx = 0
for key in self.noop:
if key == 'camera':
original_action['camera'][0] = (
self.max_camera_range
* (action[idx] - half_scale)
/ half_scale)
original_action['camera'][1] = (
self.max_camera_range
* (action[idx + 1] - half_scale)
/ half_scale)
if not self.allow_pitch:
original_action['camera'][0] = 0
idx += 2
else:
original_action[key] = int(action[idx])
idx += 1
logger.debug('discrete action {} -> original action {}'.format(action, original_action))
return original_action
class BranchedRandomizedAction(gym.ActionWrapper):
def __init__(self, env, branch_sizes, random_fraction):
super().__init__(env)
assert 0 <= random_fraction <= 1
self._random_fraction = random_fraction
self._np_random = np.random.RandomState()
def action(self, action):
if self._np_random.rand() < self._random_fraction:
action = [self._np_random.randint(n) for n in self.branch_sizes]
action = np.array(action)
return action
def seed(self, seed):
super().seed(seed)
self._np_random.seed(seed)
class BranchedActionWrapper(gym.ActionWrapper):
def __init__(self, env, branch_sizes, camera_atomic_actions,
max_range_of_camera):
super().__init__(env)
self.env = env
self.branch_sizes = branch_sizes
self.camera_atomic_actions = camera_atomic_actions
self.max_range_of_camera = max_range_of_camera
def action(self, action):
for i, branch_action in enumerate(action):
assert(branch_action >= 0 and branch_action < self.branch_sizes[i])
action_back_forward = action[0] // 3
if action_back_forward == 1:
action_back = 1
else:
action_back = 0
if action_back_forward == 2:
action_forward = 1
else:
action_forward = 0
action_left_right = action[0] % 3
if action_left_right == 1:
action_left = 1
else:
action_left = 0
if action_left_right == 2:
action_right = 1
else:
action_right = 0
action_jump = (action[1] & 1)
action_sprint = (action[1] & 2) // 2
action_sneak = (action[1] & 4) // 4
action_attack = (action[1] & 8) // 8
segment_size = 2 * self.max_range_of_camera / (self.camera_atomic_actions - 1)
camera0 = action[2] * segment_size
camera0 -= self.max_range_of_camera
camera1 = action[3] * segment_size
camera1 -= self.max_range_of_camera
#assert abs(camera0) <= self.max_range_of_camera
#assert abs(camera1) <= self.max_range_of_camera
minerl_action = {
'back': action_back,
'forward': action_forward,
'left': action_left,
'right': action_right,
'jump': action_jump,
'sprint': action_sprint,
'sneak': action_sneak,
'attack': action_attack,
'camera': np.array([camera0, camera1]),
}
if 'place' in self.env.action_space:
assert(len(action) == 5)
num_place_actions = len(self.env.action_space['place'])
if num_place_actions == 2: # Navigate envs
minerl_action['place'] = action[4]
elif num_place_actions == 7: # Obtain envs
craft = 0
equip = 0
nearbyCraft = 0
nearbySmelt = 0
place = 0
if action[4] > 0 and action[4] <= 5:
craft = action[4] - 1
elif action[4] <= 13:
equip = action[4] - 6
elif action[4] <= 21:
nearbyCraft = action[4] - 14
elif action[4] <= 24:
nearbySmelt = action[4] - 22
elif action[4] <= 31:
place = action[4] - 25
minerl_action['craft'] = craft
minerl_action['equip'] = equip
minerl_action['nearbyCraft'] = nearbyCraft
minerl_action['nearbySmelt'] = nearbySmelt
minerl_action['place'] = place
else:
raise Exception("Invalid number of place actions")
return minerl_action
class ClusteredActionWrapper(gym.ActionWrapper):
def __init__(self, env, clusters):
super().__init__(env)
self._clusters = clusters
self._np_random = np.random.RandomState()
self.action_space = gym.spaces.Discrete(len(clusters))
def action(self, action):
return {'vector': self._clusters[action]}