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mini_source_agent_dream.py
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mini_source_agent_dream.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import random
import tensorflow as tf
from pysc2.agents import base_agent
from pysc2.lib import actions as sc2_actions
from lib import utils as U
from lib import config as C
from lib import transform_pos as T
from lib import option as M
from lib import environment
from lib import my_sc2_env as sc2_env
from lib.replay_buffer import Buffer
from mini_agent import ProtossAction
from mini_network_dream import SecondNetwork
class MiniSourceAgent(base_agent.BaseAgent):
"""Agent for source game of starcraft."""
def __init__(self, index=0, rl_training=False, restore_model=False, global_buffer=None, net=None, sec_net=None,
strategy_agent=None, greedy_action=False,
extract_save_dir=None):
super(MiniSourceAgent, self).__init__()
self.net = net
self.sec_net = sec_net
self.index = index
self.global_buffer = global_buffer
self.restore_model = restore_model
# count num
self.step = 0
self.strategy_wait_secs = 2
self.strategy_flag = False
self.policy_wait_secs = 2
self.policy_flag = True
self.env = None
self.obs = None
# buffer
self.local_buffer = Buffer()
self.num_players = 2
self.on_select = None
self._result = None
self._gases = None
self.is_end = False
self.rl_training = rl_training
self.rnn_state = None
self.zero_state = self.sec_net.rnn_init_state()
self.extract_save_dir = extract_save_dir
self.reset()
def reset(self):
super(MiniSourceAgent, self).reset()
self.step = 0
self.obs = None
self._result = None
self._gases = None
self.is_end = False
self.strategy_flag = False
self.policy_flag = True
self.local_buffer.reset()
self.rnn_state = self.zero_state
def set_env(self, env):
self.env = env
def init_network(self):
self.net.initialize()
if self.restore_model:
self.net.restore_policy()
def reset_old_network(self):
self.net.reset_old_network()
def save_model(self):
self.net.save_policy()
def update_policy(self):
self.net.Update_policy(self.global_buffer)
def update_result(self, result_list):
self.net.update_result(result_list)
def update_network(self, result_list):
self.net.Update_policy(self.global_buffer)
self.net.Update_result(result_list)
def update_summary(self, counter):
return self.net.Update_summary(counter)
def mini_step(self, action):
if action == ProtossAction.Build_probe.value:
M.mineral_worker(self)
elif action == ProtossAction.Build_zealot.value:
M.train_army(self, C._TRAIN_ZEALOT)
elif action == ProtossAction.Build_Stalker.value:
M.train_army(self, C._TRAIN_STALKER)
elif action == ProtossAction.Build_pylon.value:
no_unit_index = U.get_unit_mask_screen(self.obs, size=2)
pos = U.get_pos(no_unit_index)
M.build_by_idle_worker(self, C._BUILD_PYLON_S, pos)
elif action == ProtossAction.Build_gateway.value:
power_index = U.get_power_mask_screen(self.obs, size=5)
pos = U.get_pos(power_index)
M.build_by_idle_worker(self, C._BUILD_GATEWAY_S, pos)
elif action == ProtossAction.Build_Assimilator.value:
if self._gases is not None:
#U.find_gas_pos(self.obs, 1)
gas_1 = self._gases[0]
gas_2 = self._gases[1]
if gas_1 is not None and not U.is_assimilator_on_gas(self.obs, gas_1):
gas_1_pos = T.world_to_screen_pos(self.env.game_info, gas_1.pos, self.obs)
M.build_by_idle_worker(self, C._BUILD_ASSIMILATOR_S, gas_1_pos)
elif gas_2 is not None and not U.is_assimilator_on_gas(self.obs, gas_2):
gas_2_pos = T.world_to_screen_pos(self.env.game_info, gas_2.pos, self.obs)
M.build_by_idle_worker(self, C._BUILD_ASSIMILATOR_S, gas_2_pos)
elif action == ProtossAction.Build_CyberneticsCore.value:
power_index = U.get_power_mask_screen(self.obs, size=3)
pos = U.get_pos(power_index)
M.build_by_idle_worker(self, C._BUILD_CYBER_S, pos)
elif action == ProtossAction.Attack.value:
M.attack_step(self)
elif action == ProtossAction.Retreat.value:
M.retreat_step(self)
elif action == ProtossAction.Do_nothing.value:
self.safe_action(C._NO_OP, 0, [])
def get_the_input(self):
high_input, tech_cost, pop_num = U.get_input(self.obs)
controller_input = np.concatenate([high_input, tech_cost, pop_num], axis=0)
return controller_input
def mapping_source_to_mini_by_rule(self, source_state):
simple_input = np.zeros([20], dtype=np.int16)
simple_input[0] = 0 # self.time_seconds
simple_input[1] = source_state[28] # self.mineral_worker_nums
simple_input[2] = source_state[30] + source_state[32] # self.gas_worker_nums
simple_input[3] = source_state[2] # self.mineral
simple_input[4] = source_state[3] # self.gas
simple_input[5] = source_state[6] # self.food_cup
simple_input[6] = source_state[7] # self.food_used
simple_input[7] = source_state[10] # self.army_nums
simple_input[8] = source_state[16] # self.gateway_num
simple_input[9] = source_state[14] # self.pylon_num
simple_input[10] = source_state[15] # self.Assimilator_num
simple_input[11] = source_state[17] # self.CyberneticsCore_num
simple_input[12] = source_state[12] # self.zealot_num
simple_input[13] = source_state[13] # self.Stalker_num
simple_input[14] = source_state[11] # self.probe_num
simple_input[15] = source_state[4] + source_state[2] # self.collected_mineral
simple_input[16] = source_state[4] # self.spent_mineral
simple_input[17] = source_state[5] + source_state[3] # self.collected_gas
simple_input[18] = source_state[5] # self.spent_gas
simple_input[19] = 1 # self.Nexus_num
return simple_input
def play(self, verbose=False):
self.play_train(verbose=verbose)
def sample(self, verbose=False, use_image=True):
is_attack = False
state_last = None
random_generated_int = random.randint(0, 2**31-1)
filename = self.extract_save_dir+"/"+str(random_generated_int)+".npz"
recording_obs = []
recording_img = []
recording_action = []
recording_reward = []
np.random.seed(random_generated_int)
tf.set_random_seed(random_generated_int)
self.safe_action(C._NO_OP, 0, [])
self.safe_action(C._MOVE_CAMERA, 0, [C.base_camera_pos])
self._gases = U.find_initial_gases(self.obs)
while True:
self.safe_action(C._MOVE_CAMERA, 0, [C.base_camera_pos])
if self.policy_flag and (not self.is_end):
non_image_feature = self.mapping_source_to_mini_by_rule(self.get_the_input())
#print('non_image_feature.shape:', non_image_feature.shape)
#print('non_image_feature:', non_image_feature)
image_feature = U.get_simple_map_data(self.obs)
#print('image_feature.shape:', image_feature.shape)
#print('image_feature:', image_feature)
latent_image_feature, mu, logvar = self.encode_obs(image_feature)
#print('latent_image_feature.shape:', latent_image_feature.shape)
#print('latent_image_feature:', latent_image_feature)
feature = np.concatenate([non_image_feature, latent_image_feature], axis=-1)
#print('feature.shape:', feature.shape)
#print('feature:', feature)
#state_now = feature
reward_last = 0
state_now, action, v_preds = self.get_action(feature, reward_last)
# print(ProtossAction(action).name)
self.mini_step(action)
if state_last is not None:
if False:
print('state_last:', state_last, ', action_last:', action_last, ', state_now:', state_now)
v_preds_next = self.net.policy.get_values(state_now)
v_preds_next = self.get_values(v_preds_next)
reward = 0
recording_obs.append(non_image_feature)
recording_img.append(image_feature)
recording_action.append(action)
recording_reward.append(reward)
#self.local_buffer.append(state_last, action_last, state_now, reward, v_preds, v_preds_next)
state_last = state_now
action_last = action
self.policy_flag = False
if self.is_end:
if True:
# consider the win/loss, to 0(not end), 1(loss), 2(draw), 3(win)
recording_reward[-1] = (1 * self.result['reward'] + 2)
if recording_reward[-1] != 0:
print("result is:", recording_reward[-1])
recording_obs = np.array(recording_obs, dtype=np.uint16)
recording_action = np.array(recording_action, dtype=np.uint8)
recording_reward = np.array(recording_reward, dtype=np.uint8)
recording_img = np.array(recording_img, dtype=np.float16)
np.savez_compressed(filename, obs=recording_obs, img=recording_img, action=recording_action, reward=recording_reward)
break
def play_train(self, continues_attack=False, verbose=False):
is_attack = False
state_last = None
self.safe_action(C._NO_OP, 0, [])
self.safe_action(C._MOVE_CAMERA, 0, [C.base_camera_pos])
self._gases = U.find_initial_gases(self.obs)
while True:
self.safe_action(C._MOVE_CAMERA, 0, [C.base_camera_pos])
if self.policy_flag and (not self.is_end):
non_image_feature = self.mapping_source_to_mini_by_rule(self.get_the_input())
#print('non_image_feature.shape:', non_image_feature.shape)
#print('non_image_feature:', non_image_feature)
image_feature = U.get_simple_map_data(self.obs)
#print('image_feature.shape:', image_feature.shape)
#print('image_feature:', image_feature)
latent_image_feature, mu, logvar = self.encode_obs(image_feature)
#print('latent_image_feature.shape:', latent_image_feature.shape)
#print('latent_image_feature:', latent_image_feature)
feature = np.concatenate([non_image_feature, latent_image_feature], axis=-1)
#print('feature.shape:', feature.shape)
#print('feature:', feature)
#state_now = feature
reward_last = 0
state_now, action, v_preds = self.get_action(feature, reward_last)
# print(ProtossAction(action).name)
self.mini_step(action)
if state_last is not None:
if 0:
print('state_last:', state_last, ', action_last:', action_last, ', state_now:', state_now)
v_preds_next = self.net.policy.get_values(state_now)
v_preds_next = self.get_values(v_preds_next)
reward = 0
self.local_buffer.append(state_last, action_last, state_now, reward, v_preds, v_preds_next)
state_last = state_now
action_last = action
self.policy_flag = False
if self.is_end:
if self.rl_training:
self.local_buffer.rewards[-1] += 1 * self.result['reward'] # self.result['win']
#print(self.local_buffer.rewards)
self.global_buffer.add(self.local_buffer)
#print("add %d buffer!" % (len(self.local_buffer.rewards)))
break
def encode_obs(self, obs):
# convert raw obs to z, mu, logvar
result = np.copy(obs)
result = result.reshape(1, 64, 64, 12)
mu, logvar = self.sec_net.vae.encode_mu_logvar(result)
mu = mu[0]
logvar = logvar[0]
s = logvar.shape
z = mu + np.exp(logvar/2.0) * np.random.randn(*s)
return z, mu, logvar
def get_action(self, feature, reward):
input_h = self.sec_net.rnn_output(self.rnn_state, feature)
action, v_preds = self.net.policy.get_action(input_h, verbose=False)
self.rnn_state = self.sec_net.rnn_next_state(feature, action, reward, self.rnn_state)
return input_h, action, v_preds
def set_flag(self):
if self.step % C.time_wait(self.strategy_wait_secs) == 1:
self.strategy_flag = True
if self.step % C.time_wait(self.policy_wait_secs) == 1:
self.policy_flag = True
def safe_action(self, action, unit_type, args):
if M.check_params(self, action, unit_type, args, 1):
obs = self.env.step([sc2_actions.FunctionCall(action, args)])[0]
self.obs = obs
self.step += 1
self.update_result()
self.set_flag()
def select(self, action, unit_type, args):
# safe select
if M.check_params(self, action, unit_type, args, 0):
self.obs = self.env.step([sc2_actions.FunctionCall(action, args)])[0]
self.on_select = unit_type
self.update_result()
self.step += 1
self.set_flag()
@property
def result(self):
return self._result
def update_result(self):
if self.obs is None:
return
if self.obs.last() or self.env.state == environment.StepType.LAST:
self.is_end = True
outcome = 0
o = self.obs.raw_observation
player_id = o.observation.player_common.player_id
for r in o.player_result:
if r.player_id == player_id:
outcome = sc2_env._possible_results.get(r.result, 0)
frames = o.observation.game_loop
result = {}
result['outcome'] = outcome
result['reward'] = self.obs.reward
result['frames'] = frames
self._result = result
print('play end, total return', self.obs.reward)
self.step = 0
def get_values(self, values):
# check if the game is end
if self.is_end and self.result['reward'] != 0:
return 0
else:
return values