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pre-release 0.10.0
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Gal Novik
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rl_coach/environments/doom_environment.py
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229
rl_coach/environments/doom_environment.py
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#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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try:
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import vizdoom
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except ImportError:
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from rl_coach.logger import failed_imports
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failed_imports.append("ViZDoom")
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import os
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from enum import Enum
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from os import path, environ
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from typing import Union, List
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import numpy as np
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from rl_coach.base_parameters import VisualizationParameters
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from rl_coach.environments.environment import Environment, EnvironmentParameters, LevelSelection
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from rl_coach.filters.action.full_discrete_action_space_map import FullDiscreteActionSpaceMap
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from rl_coach.filters.filter import InputFilter, OutputFilter
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from rl_coach.filters.observation.observation_rescale_to_size_filter import ObservationRescaleToSizeFilter
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from rl_coach.filters.observation.observation_stacking_filter import ObservationStackingFilter
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from rl_coach.filters.observation.observation_to_uint8_filter import ObservationToUInt8Filter
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from rl_coach.spaces import MultiSelectActionSpace, ImageObservationSpace, \
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VectorObservationSpace, StateSpace
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from rl_coach.filters.observation.observation_rgb_to_y_filter import ObservationRGBToYFilter
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# enum of the available levels and their path
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class DoomLevel(Enum):
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BASIC = "basic.cfg"
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DEFEND = "defend_the_center.cfg"
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DEATHMATCH = "deathmatch.cfg"
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MY_WAY_HOME = "my_way_home.cfg"
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TAKE_COVER = "take_cover.cfg"
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HEALTH_GATHERING = "health_gathering.cfg"
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HEALTH_GATHERING_SUPREME_COACH_LOCAL = "D2_navigation.cfg" # from https://github.com/IntelVCL/DirectFuturePrediction/tree/master/maps
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DEFEND_THE_LINE = "defend_the_line.cfg"
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DEADLY_CORRIDOR = "deadly_corridor.cfg"
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BATTLE_COACH_LOCAL = "D3_battle.cfg" # from https://github.com/IntelVCL/DirectFuturePrediction/tree/master/maps
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key_map = {
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'NO-OP': 96, # `
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'ATTACK': 13, # enter
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'CROUCH': 306, # ctrl
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'DROP_SELECTED_ITEM': ord("t"),
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'DROP_SELECTED_WEAPON': ord("t"),
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'JUMP': 32, # spacebar
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'LAND': ord("l"),
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'LOOK_DOWN': 274, # down arrow
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'LOOK_UP': 273, # up arrow
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'MOVE_BACKWARD': ord("s"),
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'MOVE_DOWN': ord("s"),
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'MOVE_FORWARD': ord("w"),
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'MOVE_LEFT': 276,
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'MOVE_RIGHT': 275,
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'MOVE_UP': ord("w"),
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'RELOAD': ord("r"),
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'SELECT_NEXT_WEAPON': ord("q"),
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'SELECT_PREV_WEAPON': ord("e"),
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'SELECT_WEAPON0': ord("0"),
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'SELECT_WEAPON1': ord("1"),
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'SELECT_WEAPON2': ord("2"),
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'SELECT_WEAPON3': ord("3"),
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'SELECT_WEAPON4': ord("4"),
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'SELECT_WEAPON5': ord("5"),
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'SELECT_WEAPON6': ord("6"),
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'SELECT_WEAPON7': ord("7"),
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'SELECT_WEAPON8': ord("8"),
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'SELECT_WEAPON9': ord("9"),
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'SPEED': 304, # shift
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'STRAFE': 9, # tab
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'TURN180': ord("u"),
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'TURN_LEFT': ord("a"), # left arrow
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'TURN_RIGHT': ord("d"), # right arrow
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'USE': ord("f"),
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}
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DoomInputFilter = InputFilter(is_a_reference_filter=True)
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DoomInputFilter.add_observation_filter('observation', 'rescaling',
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ObservationRescaleToSizeFilter(ImageObservationSpace(np.array([60, 76, 3]),
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high=255)))
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DoomInputFilter.add_observation_filter('observation', 'to_grayscale', ObservationRGBToYFilter())
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DoomInputFilter.add_observation_filter('observation', 'to_uint8', ObservationToUInt8Filter(0, 255))
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DoomInputFilter.add_observation_filter('observation', 'stacking', ObservationStackingFilter(3))
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DoomOutputFilter = OutputFilter(is_a_reference_filter=True)
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DoomOutputFilter.add_action_filter('to_discrete', FullDiscreteActionSpaceMap())
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class DoomEnvironmentParameters(EnvironmentParameters):
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def __init__(self):
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super().__init__()
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self.default_input_filter = DoomInputFilter
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self.default_output_filter = DoomOutputFilter
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self.cameras = [DoomEnvironment.CameraTypes.OBSERVATION]
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@property
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def path(self):
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return 'rl_coach.environments.doom_environment:DoomEnvironment'
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class DoomEnvironment(Environment):
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class CameraTypes(Enum):
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OBSERVATION = ("observation", "screen_buffer")
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DEPTH = ("depth", "depth_buffer")
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LABELS = ("labels", "labels_buffer")
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MAP = ("map", "automap_buffer")
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def __init__(self, level: LevelSelection, seed: int, frame_skip: int, human_control: bool,
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custom_reward_threshold: Union[int, float], visualization_parameters: VisualizationParameters,
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cameras: List[CameraTypes], **kwargs):
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super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters)
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self.cameras = cameras
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# load the emulator with the required level
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self.level = DoomLevel[level.upper()]
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local_scenarios_path = path.join(os.path.dirname(os.path.realpath(__file__)), 'doom')
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self.scenarios_dir = local_scenarios_path if 'COACH_LOCAL' in level \
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else path.join(environ.get('VIZDOOM_ROOT'), 'scenarios')
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self.game = vizdoom.DoomGame()
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self.game.load_config(path.join(self.scenarios_dir, self.level.value))
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self.game.set_window_visible(False)
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self.game.add_game_args("+vid_forcesurface 1")
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self.wait_for_explicit_human_action = True
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if self.human_control:
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self.game.set_screen_resolution(vizdoom.ScreenResolution.RES_640X480)
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elif self.is_rendered:
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self.game.set_screen_resolution(vizdoom.ScreenResolution.RES_320X240)
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else:
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# lower resolution since we actually take only 76x60 and we don't need to render
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self.game.set_screen_resolution(vizdoom.ScreenResolution.RES_160X120)
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self.game.set_render_hud(False)
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self.game.set_render_crosshair(False)
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self.game.set_render_decals(False)
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self.game.set_render_particles(False)
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for camera in self.cameras:
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if hasattr(self.game, 'set_{}_enabled'.format(camera.value[1])):
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getattr(self.game, 'set_{}_enabled'.format(camera.value[1]))(True)
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self.game.init()
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# actions
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actions_description = ['NO-OP']
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actions_description += [str(action).split(".")[1] for action in self.game.get_available_buttons()]
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actions_description = actions_description[::-1]
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self.action_space = MultiSelectActionSpace(self.game.get_available_buttons_size(),
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max_simultaneous_selected_actions=1,
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descriptions=actions_description,
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allow_no_action_to_be_selected=True)
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# human control
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if self.human_control:
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# TODO: add this to the action space
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# map keyboard keys to actions
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for idx, action in enumerate(self.action_space.descriptions):
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if action in key_map.keys():
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self.key_to_action[(key_map[action],)] = idx
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# states
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self.state_space = StateSpace({
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"measurements": VectorObservationSpace(self.game.get_state().game_variables.shape[0],
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measurements_names=[str(m) for m in
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self.game.get_available_game_variables()])
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})
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for camera in self.cameras:
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self.state_space[camera.value[0]] = ImageObservationSpace(
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shape=np.array([self.game.get_screen_height(), self.game.get_screen_width(), 3]),
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high=255)
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# seed
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if seed is not None:
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self.game.set_seed(seed)
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self.reset_internal_state()
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# render
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if self.is_rendered:
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image = self.get_rendered_image()
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self.renderer.create_screen(image.shape[1], image.shape[0])
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def _update_state(self):
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# extract all data from the current state
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state = self.game.get_state()
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if state is not None and state.screen_buffer is not None:
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self.measurements = state.game_variables
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self.state = {'measurements': self.measurements}
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for camera in self.cameras:
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observation = getattr(state, camera.value[1])
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if len(observation.shape) == 3:
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self.state[camera.value[0]] = np.transpose(observation, (1, 2, 0))
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elif len(observation.shape) == 2:
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self.state[camera.value[0]] = np.repeat(np.expand_dims(observation, -1), 3, axis=-1)
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self.reward = self.game.get_last_reward()
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self.done = self.game.is_episode_finished()
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def _take_action(self, action):
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self.game.make_action(list(action), self.frame_skip)
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def _restart_environment_episode(self, force_environment_reset=False):
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self.game.new_episode()
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def get_rendered_image(self) -> np.ndarray:
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"""
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Return a numpy array containing the image that will be rendered to the screen.
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This can be different from the observation. For example, mujoco's observation is a measurements vector.
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:return: numpy array containing the image that will be rendered to the screen
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"""
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image = [self.state[camera.value[0]] for camera in self.cameras]
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image = np.vstack(image)
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return image
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