#
# Copyright (c) 2017 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from enum import Enum
from typing import Union, List
import numpy as np
from rl_coach.filters.observation.observation_move_axis_filter import ObservationMoveAxisFilter
try:
from pysc2 import maps
from pysc2.env import sc2_env
from pysc2.env import available_actions_printer
from pysc2.lib import actions
from pysc2.lib import features
from pysc2.env import environment
from absl import app
from absl import flags
except ImportError:
from rl_coach.logger import failed_imports
failed_imports.append("PySc2")
from rl_coach.environments.environment import Environment, EnvironmentParameters, LevelSelection
from rl_coach.base_parameters import VisualizationParameters
from rl_coach.spaces import BoxActionSpace, VectorObservationSpace, PlanarMapsObservationSpace, StateSpace, CompoundActionSpace, \
DiscreteActionSpace
from rl_coach.filters.filter import InputFilter, OutputFilter
from rl_coach.filters.observation.observation_rescale_to_size_filter import ObservationRescaleToSizeFilter
from rl_coach.filters.action.linear_box_to_box_map import LinearBoxToBoxMap
from rl_coach.filters.observation.observation_to_uint8_filter import ObservationToUInt8Filter
FLAGS = flags.FLAGS
FLAGS(['coach.py'])
SCREEN_SIZE = 84 # will also impact the action space size
# Starcraft Constants
_NOOP = actions.FUNCTIONS.no_op.id
_MOVE_SCREEN = actions.FUNCTIONS.Move_screen.id
_SELECT_ARMY = actions.FUNCTIONS.select_army.id
_PLAYER_RELATIVE = features.SCREEN_FEATURES.player_relative.index
_NOT_QUEUED = [0]
_SELECT_ALL = [0]
class StarcraftObservationType(Enum):
Features = 0
RGB = 1
StarcraftInputFilter = InputFilter(is_a_reference_filter=True)
StarcraftInputFilter.add_observation_filter('screen', 'move_axis', ObservationMoveAxisFilter(0, -1))
StarcraftInputFilter.add_observation_filter('screen', 'rescaling',
ObservationRescaleToSizeFilter(
PlanarMapsObservationSpace(np.array([84, 84, 1]),
low=0, high=255, channels_axis=-1)))
StarcraftInputFilter.add_observation_filter('screen', 'to_uint8', ObservationToUInt8Filter(0, 255))
StarcraftInputFilter.add_observation_filter('minimap', 'move_axis', ObservationMoveAxisFilter(0, -1))
StarcraftInputFilter.add_observation_filter('minimap', 'rescaling',
ObservationRescaleToSizeFilter(
PlanarMapsObservationSpace(np.array([64, 64, 1]),
low=0, high=255, channels_axis=-1)))
StarcraftInputFilter.add_observation_filter('minimap', 'to_uint8', ObservationToUInt8Filter(0, 255))
StarcraftNormalizingOutputFilter = OutputFilter(is_a_reference_filter=True)
StarcraftNormalizingOutputFilter.add_action_filter(
'normalization', LinearBoxToBoxMap(input_space_low=-SCREEN_SIZE / 2, input_space_high=SCREEN_SIZE / 2 - 1))
class StarCraft2EnvironmentParameters(EnvironmentParameters):
def __init__(self, level=None):
super().__init__(level=level)
self.screen_size = 84
self.minimap_size = 64
self.feature_minimap_maps_to_use = range(7)
self.feature_screen_maps_to_use = range(17)
self.observation_type = StarcraftObservationType.Features
self.disable_fog = False
self.auto_select_all_army = True
self.default_input_filter = StarcraftInputFilter
self.default_output_filter = StarcraftNormalizingOutputFilter
self.use_full_action_space = False
@property
def path(self):
return 'rl_coach.environments.starcraft2_environment:StarCraft2Environment'
# Environment
[docs]class StarCraft2Environment(Environment):
def __init__(self, level: LevelSelection, frame_skip: int, visualization_parameters: VisualizationParameters,
target_success_rate: float=1.0, seed: Union[None, int]=None, human_control: bool=False,
custom_reward_threshold: Union[int, float]=None,
screen_size: int=84, minimap_size: int=64,
feature_minimap_maps_to_use: List=range(7), feature_screen_maps_to_use: List=range(17),
observation_type: StarcraftObservationType=StarcraftObservationType.Features,
disable_fog: bool=False, auto_select_all_army: bool=True,
use_full_action_space: bool=False, **kwargs):
super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters, target_success_rate)
self.screen_size = screen_size
self.minimap_size = minimap_size
self.feature_minimap_maps_to_use = feature_minimap_maps_to_use
self.feature_screen_maps_to_use = feature_screen_maps_to_use
self.observation_type = observation_type
self.features_screen_size = None
self.feature_minimap_size = None
self.rgb_screen_size = None
self.rgb_minimap_size = None
if self.observation_type == StarcraftObservationType.Features:
self.features_screen_size = screen_size
self.feature_minimap_size = minimap_size
elif self.observation_type == StarcraftObservationType.RGB:
self.rgb_screen_size = screen_size
self.rgb_minimap_size = minimap_size
self.disable_fog = disable_fog
self.auto_select_all_army = auto_select_all_army
self.use_full_action_space = use_full_action_space
# step_mul is the equivalent to frame skipping. Not sure if it repeats actions in between or not though.
self.env = sc2_env.SC2Env(map_name=self.env_id, step_mul=frame_skip,
visualize=self.is_rendered,
agent_interface_format=sc2_env.AgentInterfaceFormat(
feature_dimensions=sc2_env.Dimensions(
screen=self.features_screen_size,
minimap=self.feature_minimap_size
)
# rgb_dimensions=sc2_env.Dimensions(
# screen=self.rgb_screen_size,
# minimap=self.rgb_screen_size
# )
),
# feature_screen_size=self.features_screen_size,
# feature_minimap_size=self.feature_minimap_size,
# rgb_screen_size=self.rgb_screen_size,
# rgb_minimap_size=self.rgb_screen_size,
disable_fog=disable_fog,
random_seed=self.seed
)
# print all the available actions
# self.env = available_actions_printer.AvailableActionsPrinter(self.env)
self.reset_internal_state(True)
"""
feature_screen: [height_map, visibility_map, creep, power, player_id, player_relative, unit_type, selected,
unit_hit_points, unit_hit_points_ratio, unit_energy, unit_energy_ratio, unit_shields,
unit_shields_ratio, unit_density, unit_density_aa, effects]
feature_minimap: [height_map, visibility_map, creep, camera, player_id, player_relative, selecte
d]
player: [player_id, minerals, vespene, food_cap, food_army, food_workers, idle_worker_dount,
army_count, warp_gate_count, larva_count]
"""
self.screen_shape = np.array(self.env.observation_spec()[0]['feature_screen'])
self.screen_shape[0] = len(self.feature_screen_maps_to_use)
self.minimap_shape = np.array(self.env.observation_spec()[0]['feature_minimap'])
self.minimap_shape[0] = len(self.feature_minimap_maps_to_use)
self.state_space = StateSpace({
"screen": PlanarMapsObservationSpace(shape=self.screen_shape, low=0, high=255, channels_axis=0),
"minimap": PlanarMapsObservationSpace(shape=self.minimap_shape, low=0, high=255, channels_axis=0),
"measurements": VectorObservationSpace(self.env.observation_spec()[0]["player"][0])
})
if self.use_full_action_space:
action_identifiers = list(self.env.action_spec()[0].functions)
num_action_identifiers = len(action_identifiers)
action_arguments = [(arg.name, arg.sizes) for arg in self.env.action_spec()[0].types]
sub_action_spaces = [DiscreteActionSpace(num_action_identifiers)]
for argument in action_arguments:
for dimension in argument[1]:
sub_action_spaces.append(DiscreteActionSpace(dimension))
self.action_space = CompoundActionSpace(sub_action_spaces)
else:
self.action_space = BoxActionSpace(2, 0, self.screen_size - 1, ["X-Axis, Y-Axis"],
default_action=np.array([self.screen_size/2, self.screen_size/2]))
self.target_success_rate = target_success_rate
def _update_state(self):
timestep = 0
self.screen = self.last_result[timestep].observation.feature_screen
# extract only the requested segmentation maps from the observation
self.screen = np.take(self.screen, self.feature_screen_maps_to_use, axis=0)
self.minimap = self.last_result[timestep].observation.feature_minimap
self.measurements = self.last_result[timestep].observation.player
self.reward = self.last_result[timestep].reward
self.done = self.last_result[timestep].step_type == environment.StepType.LAST
self.state = {
'screen': self.screen,
'minimap': self.minimap,
'measurements': self.measurements
}
def _take_action(self, action):
if self.use_full_action_space:
action_identifier = action[0]
action_arguments = action[1:]
action = actions.FunctionCall(action_identifier, action_arguments)
else:
coord = np.array(action[0:2])
noop = False
coord = coord.round()
coord = np.clip(coord, 0, SCREEN_SIZE - 1)
self.last_action_idx = coord
if noop:
action = actions.FunctionCall(_NOOP, [])
else:
action = actions.FunctionCall(_MOVE_SCREEN, [_NOT_QUEUED, coord])
self.last_result = self.env.step(actions=[action])
def _restart_environment_episode(self, force_environment_reset=False):
# reset the environment
self.last_result = self.env.reset()
# select all the units on the screen
if self.auto_select_all_army:
self.env.step(actions=[actions.FunctionCall(_SELECT_ARMY, [_SELECT_ALL])])
def get_rendered_image(self):
screen = np.squeeze(np.tile(np.expand_dims(self.screen, -1), (1, 1, 3)))
screen = screen / np.max(screen) * 255
return screen.astype('uint8')
def dump_video_of_last_episode(self):
from rl_coach.logger import experiment_path
self.env._run_config.replay_dir = experiment_path
self.env.save_replay('replays')
super().dump_video_of_last_episode()
def get_target_success_rate(self):
return self.target_success_rate