def train_action(self, obs, save_path):
if self.init_static_agent_type is None:
self.init_static_agent_type = get_init_static_agent_type(obs)
self.vaild_action = get_specified_agent_all_valid_action(
self.init_static_agent_type)
# self.controller.network.action_dim = self.action_dim
# self.controller.network.net.action_dim = self.action_dim
# self.controller = decision_maker(
# net(config.MU, config.SIGMA, config.LEARING_RATE, self.action_dim,
# self.state_data_shape, config.MY_UNIT_NUMBER,
# config.ENEMY_UNIT_NUMBER, 'attack_controller'))
# self.controller.network.valid_action = self.vaild_action
self.controller.network.init_static_agent_type = self.init_static_agent_type
if obs.first():
self.init_tags = get_init_tags(obs, self.init_static_agent_type)
agents_obs = np.array(get_agents_obs(self.init_tags, obs))
self.controller.current_state = [
np.array(
get_bound(agents_obs, self.init_static_agent_type,
self.action_dim)), agents_obs
]
self.current_obs = obs
if self.controller.previous_action is not None:
self.controller.previous_reward = get_reward(obs, self.pre_obs)
self.controller.network.perceive(self.controller.previous_state,
self.controller.previous_action,
self.controller.previous_reward,
self.controller.current_state,
win_or_loss(obs), save_path)
if obs.last():
self.controller.previous_state = None
self.controller.previous_action = None
self.controller.previous_reward = None
self.init_tags = None
self.pre_obs = None
self.current_obs = None
return RAW_FUNCTIONS.no_op()
else:
action = self.controller.network.egreedy_action(
self.controller.current_state)
actions = handcraft_function_for_level_2_attack_controller.assembly_action(
self.init_tags, obs, action, self.vaild_action)
self.controller.previous_state = self.controller.current_state
self.controller.previous_action = action
self.pre_obs = self.current_obs
return actions
def test_action(self, obs):
obs_new = get_clusters_test(obs)
if len(obs_new) == 0:
return RAW_FUNCTIONS.no_op()
else:
actions = []
bounds_and_states, my_units_and_enemy_units_pack = get_bounds_and_states(
obs_new)
for i in range(len(bounds_and_states)):
action_number = self.controller.network.action(
bounds_and_states[i])
action = assembly_action_test(
my_units_and_enemy_units_pack[i][0],
my_units_and_enemy_units_pack[i][1], action_number)
actions += action
return actions
def train_action(self, obs, save_path):
if obs.first():
self.init_obs = obs
self.controller.current_state = [
np.array(get_bound(self.init_obs, obs)),
np.array(get_state(self.init_obs, obs)),
np.array(get_agents_obs(self.init_obs, obs))
]
self.current_obs = obs
if self.controller.previous_action is not None:
self.controller.previous_reward = get_reward(
self.current_obs, self.pre_obs)
self.controller.network.perceive(self.controller.previous_state,
self.controller.previous_action,
self.controller.previous_reward,
self.controller.current_state,
win_or_loss(obs), save_path)
if obs.last():
self.controller.previous_state = None
self.controller.previous_action = None
self.controller.previous_reward = None
self.init_obs = None
self.pre_obs = None
self.current_obs = None
return RAW_FUNCTIONS.no_op()
else:
action = self.controller.network.egreedy_action(
self.controller.current_state)
actions = handcraft_function_for_level_2_attack_controller.assembly_action(
self.init_obs, obs, action)
self.controller.previous_state = self.controller.current_state
self.controller.previous_action = action
self.pre_obs = self.current_obs
return actions