def create_game(
environment_config: EnvironmentConfig
) -> t.Tuple[DoomGame, ActionList]:
"""Creates an instance of VizDoom.
Args:
scenario: The name of the scenario to play.
environment_config: An environment configuration instance.
Returns:
A Doom game instance that respects OpenAI's gym interface.
"""
game = DoomGame()
# Game configuration
game.load_config(f'{paths.SCENARIOS}/{environment_config.scenario}.cfg')
game.set_doom_scenario_path(
f'{paths.SCENARIOS}/{environment_config.scenario}.wad')
game.set_mode(environment_config.game_mode)
game.set_screen_format(environment_config.screen_mode)
game.init()
possible_actions = controls.get_available_actions(
game.get_available_buttons())
return game, possible_actions
class DoomTrainer:
def __init__(self, params):
self.game = DoomGame()
self.game.load_config("../scenarios/" + params.scenario + ".cfg")
if params.model == 'human':
self.game.set_mode(Mode.SPECTATOR)
else:
self.actions = create_actions(params.scenario)
def play_human(self):
episodes = 10
for i in range(episodes):
self.game.new_episode()
while not self.game.is_episode_finished():
self.game.get_state()
self.game.advance_action()
self.game.get_last_action()
self.game.get_last_reward()
def start_game(self):
self.game.init()
def set_seed(self, seed):
self.game.set_seed(seed)
def new_episode(self):
self.game.new_episode()
def get_screen(self):
return torch.from_numpy(
scale(self.game.get_state().screen_buffer, None, None, True))
def get_screen_buffer(self):
return self.game.get_state().screen_buffer
def make_action(self, action):
reward = self.game.make_action(self.actions[action])
done = self.game.is_episode_finished()
return reward, done
def num_actions(self):
return len(self.actions)