# rl_coach.environments.starcraft2_environment — Reinforcement Learning Coach 0.12.0 documentation

# Source code for rl_coach.environments.starcraft2_environment

    #
    # 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
    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