def update_master_policy(self,rbs,disc, lr, cter):
samples =random.sample(rbs,batch_size)
minimaps = []
screens = []
infos = []
next_minimaps = []
next_screens = []
next_infos = []
actions = []
rewards = []
for i,[obs,_,action,_,next_obs] in enumerate(samples):
minimap = np.array(obs.observation['minimap'], dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
next_minimap = np.array(next_obs.observation['minimap'], dtype=np.float32)
next_minimap = np.expand_dims(U.preprocess_minimap(next_minimap), axis=0)
next_screen = np.array(obs.observation['screen'], dtype=np.float32)
next_screen = np.expand_dims(U.preprocess_screen(next_screen), axis=0)
next_info = np.zeros([1, self.isize], dtype=np.float32)
next_info[0, obs.observation['available_actions']] = 1
reward = next_obs.reward
minimaps.append(minimap)
screens.append(screen)
infos.append(info)
next_minimaps.append(next_minimap)
next_screens.append(next_screen)
next_infos.append(next_info)
cur_action = np.zeros(num_subpolicies)
cur_action[action]=1
actions.append(cur_action)
rewards.append(reward)
minimaps = np.concatenate(minimaps, axis=0)
screens = np.concatenate(screens, axis=0)
infos = np.concatenate(infos, axis=0)
next_minimaps = np.concatenate(next_minimaps, axis=0)
next_screens = np.concatenate(next_screens, axis=0)
next_infos = np.concatenate(next_infos, axis=0)
y_batch = []
Qvalue_batch =self.sess_master.run(self.subpolicy_Q,feed_dict = {self.minimap: next_minimaps,
self.screen: next_screens,
self.info: next_infos})
for i in range(0, batch_size):
terminal = samples[i][3]
if terminal:
y_batch.append(rewards[i])
else:
y_batch.append(rewards[i] + disc * np.max(Qvalue_batch[i]))
self.sess_master.run(self.master_train_op, feed_dict={self.minimap:minimaps,
self.screen:screens,
self.info:infos,
self.y_input:y_batch,
self.action_input:actions,
self.learning_rate:lr})
def calc_pixel_change(obs2, obs1):
screen1 = np.array(obs1.observation['screen'], dtype=np.float32)
screen1 = np.expand_dims(U.preprocess_screen(screen1), axis=0)
screen2 = np.array(obs2.observation['screen'], dtype=np.float32)
screen2 = np.expand_dims(U.preprocess_screen(screen2), axis=0)
screen1_avg = np.mean(screen1, axis=0)
screen2_avg = np.mean(screen1, axis=0)
d = np.absolute(screen2_avg[2:-2, 2:-2, :] - screen1_avg[2:-2, 2:-2, :])
# (60,60,3), any channel here? but with the np.mean on next line, doesn't matter either way.
m = np.mean(d, 2)
pc = self._subsample(m, 3)
return pc
def getTrainFeedDict(self, obs, action, attributed_act_id):
screen = np.array(obs.observation['feature_screen'], dtype=np.float32)
screen = U.preprocess_screen(screen)
info = np.zeros([len(U.useful_actions)], dtype=np.float32)
info[U.compressActions(obs.observation['available_actions'])] = 1
valid_spatial_action = 0
valid_action = np.zeros([len(U.useful_actions)], dtype=np.float32)
custom_inputs = np.array(obs.observation.custom_inputs, dtype=np.float32)
act_id = action.function
net_act_id = attributed_act_id
act_args = action.arguments
player_relative = obs.observation.feature_screen.player_relative
valid_actions = obs.observation["available_actions"]
valid_actions = U.compressActions(valid_actions)
valid_action[valid_actions] = 1
args = actions.FUNCTIONS[act_id].args
for arg, act_arg in zip(args, act_args):
if arg.name in ('screen', 'minimap', 'screen2') and (not self.flags.force_focus_fire or (act_id != 12 and act_id != 2)):
valid_spatial_action = 1
return {
self.screen: screen, # yes
self.info: info, # yes
self.custom_inputs: custom_inputs, #yes
self.valid_spatial_action: valid_spatial_action, #yes
self.valid_action: valid_action, # yes
}
def step_low(self, ind_thread, obs, dir_high, act_id):
# obs就是环境传入的timestep
minimap = np.array(
obs.observation['feature_minimap'], dtype=np.float32
) # 以下4行将minimap和screen的特征做一定处理后分别保存在minimap和screen变量中
minimap = np.expand_dims(U.preprocess_minimap(minimap),
axis=0) # 这四行具体语法暂未研究
screen = np.array(obs.observation['feature_screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
# TODO: only use available actions
info = np.zeros([1, self.isize],
dtype=np.float32) # self.isize值是动作函数的数量
info[0, obs.observation['available_actions']] = 1 # info存储可执行的动作。
# 矿物 军队数量 农民数量
info_plus = np.zeros([1, self.info_plus_size], dtype=np.float32)
info_plus[0] = obs.observation.player.minerals, obs.observation[
'player'][5], obs.observation['player'][6], obs.observation[
'player'][4]
# info 现在的size 是 isize + info_plus_size
info = np.concatenate((info, info_plus), axis=1)
dir_high_usedToFeedLowNet = np.ones([1, 1], dtype=np.float32)
dir_high_usedToFeedLowNet[0][0] = dir_high
act_ID = np.ones([1, 1], dtype=np.float32)
act_ID[0][0] = act_id
feed = {
self.minimap: minimap,
self.screen: screen,
self.info: info,
self.dir_high_usedToFeedLowNet: dir_high_usedToFeedLowNet,
self.act_id: act_ID
}
spatial_action_low = self.sess.run( # 数据类型：Tensor("actor_low/Softmax:0", shape=(?, 4096), dtype=float32, device=/device:GPU:0)
# [array([[0.00019935, 0.00025348, 0.00024519, ..., 0.00016189, 0.00016014, 0.00016842]], dtype=float32)]
[self.spatial_action_low],
feed_dict=feed)
# 选择施加动作的位置
# spatial_action_low = spatial_action_low.ravel() # ravel()是numpy的函数，作用是将数据降维
target = np.argmax(spatial_action_low)
target = [int(target // self.ssize),
int(target % self.ssize)
] # 获取要施加动作的位置 疑问：若action是勾选方框怎么办？target只有一个坐标吧，那另一个坐标呢？
# if False: # 疑问：if False什么意思？网上没查到
# print(actions.FUNCTIONS[act_id].name, target)
# Epsilon greedy exploration # 0.2(epsilon[1])的概率随机选一个位置施加动作
if self.training and np.random.rand() < self.epsilon[1]:
dy = np.random.randint(-4, 5)
target[0] = int(max(0, min(self.ssize - 1, target[0] + dy)))
dx = np.random.randint(-4, 5)
target[1] = int(max(0, min(self.ssize - 1, target[1] + dx)))
return target[0], target[1]
def step(self, obs):
minimap = np.array(obs.observation['minimap'], dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
# TODO: only use available actions
action_indices = [
0, 1, 2, 3, 5, 7, 12, 13, 14, 15, 18, 19, 20, 261, 274, 331, 332,
333, 334, 451, 452, 453, 456
]
valid_actions = list(
set(obs.observation['available_actions']) & set(action_indices))
# print("valid_actions",valid_actions)
valid_actions_indices = [
action_indices.index(i) for i in valid_actions
]
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, valid_actions_indices] = 1
feed = {self.minimap: minimap, self.screen: screen, self.info: info}
non_spatial_action, spatial_action = self.sess.run(
[self.non_spatial_action, self.spatial_action], feed_dict=feed)
# print("non_spatial_action",non_spatial_action.shape,len(non_spatial_action.ravel()))
# print("spatial_action",spatial_action.ravel().shape,len(spatial_action.ravel()))
# Select an action and a spatial target
non_spatial_action = non_spatial_action.ravel()
spatial_action = spatial_action.ravel()
# valid_actions = obs.observation['available_actions']
# act_id = valid_actions[np.argmax(non_spatial_action[valid_actions])]
# print("valid",non_spatial_action[valid_actions_indices])
act_id = valid_actions[np.argmax(
non_spatial_action[valid_actions_indices])]
# print("SELECTED",act_id)
target = np.argmax(spatial_action)
target = [int(target // self.ssize), int(target % self.ssize)]
# Epsilon greedy exploration
if self.training and np.random.rand() < self.epsilon[0]:
act_id = np.random.choice(valid_actions)
if self.training and np.random.rand() < self.epsilon[1]:
dy = np.random.randint(-4, 5)
target[0] = int(max(0, min(self.ssize - 1, target[0] + dy)))
dx = np.random.randint(-4, 5)
target[1] = int(max(0, min(self.ssize - 1, target[1] + dx)))
# Set act_id and act_args
act_args = []
for arg in actions.FUNCTIONS[act_id].args:
if arg.name in ('screen', 'minimap', 'screen2'):
act_args.append([target[1], target[0]])
else:
act_args.append([0]) # TODO: Be careful
return actions.FunctionCall(act_id, act_args)
def step(self, obs):
minimap = np.array(obs.observation['feature_minimap'],
dtype=np.float32)
# self.logger.info(minimap)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['feature_screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
# TODO: only use available actions
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
feed = {self.minimap: minimap, self.screen: screen, self.info: info}
non_spatial_action, spatial_action = self.sess.run(
[self.non_spatial_action, self.spatial_action], feed_dict=feed)
# Select an action and a spatial target
non_spatial_action = non_spatial_action.ravel(
) # .ravel flattens the input into 1D array
spatial_action = spatial_action.ravel()
valid_actions = obs.observation['available_actions']
act_id = valid_actions[np.argmax(
non_spatial_action[valid_actions]
)] # index of best valid non-spatial action
target = np.argmax(spatial_action)
target = [
int(target // self.ssize),
int(target % self.ssize)
] # int(idx//32), int(idx%32) # what is the purpose of doing like this?
if False: # ???
self.logger.info(actions.FUNCTIONS[act_id].name, target)
# Epsilon greedy exploration.
if self.training and np.random.rand(
) < self.epsilon[0]: # choose action
act_id = np.random.choice(valid_actions)
if self.training and np.random.rand(
) < self.epsilon[1]: # seems like its not choosing from ALL pixels?
dy = np.random.randint(-4, 5)
target[0] = int(max(0, min(self.ssize - 1, target[0] +
dy))) # max(0, min(31, target[0]+dy))
dx = np.random.randint(-4, 5)
target[1] = int(max(0, min(self.ssize - 1, target[1] + dx)))
# Set act_id and act_args
act_args = []
for arg in actions.FUNCTIONS[
act_id].args: # find args of indexed action
if arg.name in ('screen', 'minimap', 'screen2'):
act_args.append([target[1], target[0]])
else:
act_args.append([0]) # TODO: Be careful (???)
return actions.FunctionCall(act_id, act_args)
def step(self, obs):
"""
choose action
get observation, return spatial, nonspatial action using RL
obs = observation spec in lib/features.py : 218
"""
# for v in tf.get_default_graph().as_graph_def().node:
# print(v.name)
# obs.observation.screen_feature is (17, 64, 64)
screen = np.array(obs.observation.feature_screen, dtype=np.float32)
screen = np.expand_dims(preprocess_screen(screen), axis=0) # return (bs=1, channel=42, h=64, w=64)
# get available actions
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
# run session to obtain spatial action output and non spatial action array
non_spatial_action, spatial_action = self.sess.run(
[self.non_spatial_action, self.spatial_action], feed_dict={self.screen: screen, self.info: info})
# select action and spatial target
non_spatial_action = non_spatial_action.ravel() # flatten
spatial_action = spatial_action.ravel() # flatten
valid_actions = obs.observation['available_actions'] # available action index
act_id = valid_actions[np.argmax(non_spatial_action[valid_actions])]
target = np.argmax(spatial_action) # position to move
target = [int(target // self.ssize), int(target % self.ssize)]
# e-greedy action selection (IN THIS NETWORK, WE EXPLORE ONLY IF A RANDOM FRACTION IS ABOVE EPSILON)
if np.random.random() > self.epsilon:
# randomly select non-spatial action
act_id = np.random.choice(valid_actions)
# randomly select spatial action
dy = np.random.randint(-4, 5)
target[0] = int(max(0, min(self.ssize - 1, target[0] + dy)))
dx = np.random.randint(-4, 5)
target[1] = int(max(0, min(self.ssize - 1, target[1] + dx)))
# Set act_id and act_args
act_args = []
for arg in actions.FUNCTIONS[act_id].args:
if arg.name in ('screen', 'minimap', 'screen2'): # in fact, only screen
act_args.append([target[1], target[0]]) # y x to x y
else:
act_args.append([0]) # [0] means not queue
# self.steps += 1
# self.reward += obs.reward
# print("return action with id: {} and args {} ".format(act_id, act_args))
return actions.FunctionCall(act_id, act_args)
def step(self, obs):
screen = np.array(obs.observation.feature_screen, dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
minimap = np.array(obs.observation.feature_minimap, dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
structured = np.zeros([1, self.structured_dimensions],
dtype=np.float32)
structured[0, obs.observation.available_actions] = 1
feed_dict = {
self.screen_ph: screen,
self.minimap_ph: minimap,
self.structured_ph: structured
}
non_spatial_action, spatial_action = self.sess.run(
[self.non_spatial_action, self.spatial_action],
feed_dict=feed_dict)
non_spatial_action, spatial_action = non_spatial_action.ravel(
), spatial_action.ravel()
available_actions = obs.observation.available_actions
action_id = available_actions[np.argmax(
non_spatial_action[available_actions])]
spatial_target = np.argmax(spatial_action)
spatial_target = [
int(spatial_target // self.screen_dimensions),
int(spatial_target % self.screen_dimensions)
]
# epsilon-greedy exploration
if self.training and np.random.rand() < self.epsilon[0]:
action_id = np.random.choice(available_actions)
if self.training and np.random.rand() < self.epsilon[1]:
delta_y, delta_x = np.random.randint(-4,
5), np.random.randint(-4, 5)
spatial_target[0] = int(
max(
0,
min(self.screen_dimensions - 1,
spatial_target[0] + delta_y)))
spatial_target[1] = int(
max(
0,
min(self.screen_dimensions - 1,
spatial_target[1] + delta_x)))
action_args = []
for arg in actions.FUNCTIONS[action_id].args:
if arg.name in ('screen', 'minimap', 'screen2'):
action_args.append([spatial_target[1], spatial_target[0]])
else:
action_args.append([0])
return actions.FunctionCall(action_id, action_args)
def get_cur_Q_action(self,obs):
minimap = np.array(obs.observation['minimap'], dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
# TODO: only use available actions
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
feed_master = {self.minimap: minimap,
self.screen: screen,
self.info: info}
subpolicy_selected = np.argmax(self.sess_master.run(self.subpolicy_Q, feed_dict=feed_master),axis=1)[0]
return subpolicy_selected
def step(self, obs):
minimap = np.array(obs.observation['minimap'], dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
# TODO: only use available actions
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
subpolicy_index = self.get_cur_Q_action(obs)
#print("Subpolicy Chosen is :"+str(subpolicy_index))
feed = {self.minimap: minimap,
self.screen: screen,
self.info: info}
cur_spatial_action,cur_non_spation_action,_ = self.subpolicies[subpolicy_index]
non_spatial_action, spatial_action = self.sess_master.run(
[cur_non_spation_action, cur_spatial_action],
feed_dict=feed)
# Select an action and a spatial target
non_spatial_action = non_spatial_action.ravel()
spatial_action = spatial_action.ravel()
valid_actions = obs.observation['available_actions']
act_id = valid_actions[np.argmax(non_spatial_action[valid_actions])]
target = np.argmax(spatial_action)
target = [int(target // self.ssize), int(target % self.ssize)]
if False:
print(actions.FUNCTIONS[act_id].name, target)
# Epsilon greedy exploration
if self.training and np.random.rand() < self.epsilon[0]:
act_id = np.random.choice(valid_actions)
if self.training and np.random.rand() < self.epsilon[1]:
dy = np.random.randint(-4, 5)
target[0] = int(max(0, min(self.ssize - 1, target[0] + dy)))
dx = np.random.randint(-4, 5)
target[1] = int(max(0, min(self.ssize - 1, target[1] + dx)))
# Set act_id and act_args
act_args = []
for arg in actions.FUNCTIONS[act_id].args:
if arg.name in ('screen', 'minimap', 'screen2'):
act_args.append([target[1], target[0]])
else:
act_args.append([0]) # TODO: Be careful
return actions.FunctionCall(act_id, act_args)
def getPredictFeedDict(self, obs, hState, cState):
screen = np.array(obs.observation['feature_screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, len(U.useful_actions)], dtype=np.float32)
info[0, U.compressActions(obs.observation['available_actions'])] = 1
custom_inputs = np.expand_dims(
np.array(obs.observation.custom_inputs, dtype=np.float32), axis=0)
hState = np.expand_dims(np.array(hState), axis=0)
cState = np.expand_dims(np.array(cState), axis=0)
return {
self.screen: screen,
self.info: info,
self.custom_inputs: custom_inputs,
self.hStateInput: hState,
self.cStateInput: cState }
def step(self, obs):
super(A3CAgent, self).step(obs)
minimap = np.array(obs.observation['feature_minimap'],
dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['feature_screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
# TODO: only use available actions
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.
observation['available_actions']] = 1 #mask for available actions
feed = {self.minimap: minimap, self.screen: screen, self.info: info}
non_spatial_action, spatial_action = self.sess.run(
[self.non_spatial_action, self.spatial_action], feed_dict=feed)
# Select an action and a spatial target
non_spatial_action = non_spatial_action.ravel()
spatial_action = spatial_action.ravel()
valid_actions = obs.observation['available_actions']
act_id = valid_actions[np.argmax(non_spatial_action[valid_actions])]
target = np.argmax(spatial_action)
target = [int(target // self.ssize), int(target % self.ssize)]
if False:
print(actions.FUNCTIONS[act_id].name, target)
# Epsilon greedy exploration
if self.training and np.random.rand() < self.epsilon[0]:
act_id = np.random.choice(valid_actions)
if self.training and np.random.rand() < self.epsilon[1]:
dy = np.random.randint(-4, 5)
target[0] = int(max(0, min(self.ssize - 1, target[0] + dy)))
dx = np.random.randint(-4, 5)
target[1] = int(max(0, min(self.ssize - 1, target[1] + dx)))
# Set act_id and act_args
act_args = []
for arg in actions.FUNCTIONS[act_id].args:
if arg.name in ('screen', 'minimap', 'screen2'):
act_args.append([target[1],
target[0]]) #use (y,x)->(height,width)
else:
act_args.append([0]) #not queued TODO: Be careful
return actions.FunctionCall(act_id, act_args)
def step(self, obs):
minimap = np.array(obs.observation['minimap'], dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
# TODO: only use available actions
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
feed = {self.minimap: minimap,
self.screen: screen,
self.info: info}
non_spatial_action, spatial_action = self.sess.run(
[self.non_spatial_action, self.spatial_action],
feed_dict=feed)
# Select an action and a spatial target
non_spatial_action = non_spatial_action.ravel()
spatial_action = spatial_action.ravel()
valid_actions = obs.observation['available_actions']
act_id = valid_actions[np.argmax(non_spatial_action[valid_actions])]
target = np.argmax(spatial_action)
target = [int(target // self.ssize), int(target % self.ssize)]
if False:
print(actions.FUNCTIONS[act_id].name, target)
# Epsilon greedy exploration
if self.training and np.random.rand() < self.epsilon[0]:
act_id = np.random.choice(valid_actions)
if self.training and np.random.rand() < self.epsilon[1]:
dy = np.random.randint(-4, 5)
target[0] = int(max(0, min(self.ssize-1, target[0]+dy)))
dx = np.random.randint(-4, 5)
target[1] = int(max(0, min(self.ssize-1, target[1]+dx)))
# Set act_id and act_args
act_args = []
for arg in actions.FUNCTIONS[act_id].args:
if arg.name in ('screen', 'minimap', 'screen2'):
act_args.append([target[1], target[0]])
else:
act_args.append([0]) # TODO: Be careful
return actions.FunctionCall(act_id, act_args)
def step_high(self, obs): # obs就是环境传入的timestep
minimap = np.array(
obs.observation['feature_minimap'], dtype=np.float32
) # 以下4行将minimap和screen的特征做一定处理后分别保存在minimap和screen变量中
minimap = np.expand_dims(U.preprocess_minimap(minimap),
axis=0) # 这四行具体语法暂未研究
screen = np.array(obs.observation['feature_screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
# TODO: only use available actions
info = np.zeros([1, self.isize],
dtype=np.float32) # self.isize值是动作函数的数量
info[0, obs.observation['available_actions']] = 1 # info存储可执行的动作。
# 矿物 军队数量 农民数量
info_plus = np.zeros([1, self.info_plus_size], dtype=np.float32)
info_plus[0] = obs.observation.player.minerals, obs.observation[
'player'][5], obs.observation['player'][6], obs.observation[
'player'][4]
# info 现在的size 是 isize + info_plus_size
info = np.concatenate((info, info_plus), axis=1)
feed = {self.minimap: minimap, self.screen: screen, self.info: info}
dir_high = self.sess.run([self.dir_high], feed_dict=feed)
# 选择出宏动作的编号/id
#DHN待处理： 可以将dir_high先根据一定的方法筛选一下（比如宏动作中的硬编码微动作是否在obs.observation['available_actions']中）
# valid_dir_high = obs.observation['available_actions']
dir_high_id = np.argmax(dir_high) # 获取要执行的宏动作id（从0开始）
# if False: # 疑问：if False什么意思？网上没查到
# print(actions.FUNCTIONS[act_id].name, target)
# Epsilon greedy exploration # 0.05(epsilon[0])的概率随机选一个宏动作（会覆盖之前的dir_high_id）
if self.training and np.random.rand() < self.epsilon[0]:
dir_high_id = random.randint(0, num_macro_action - 1)
return dir_high_id
def update(self, rbs, disc, lr, cter):
# Compute R, which is value of the last observation
obs = rbs[-1][-1]
# Print out score on a test run through a full episode, don't update network on test run
if self.test_run and obs.last():
self.test_scores.append(obs.observation['score_cumulative'][0])
# print("TEST SCORE: " + str(self.test_scores[-1]))
return
else:
train_score = obs.observation['score_cumulative'][0]
logger.info('Total game steps: %s', self.count_steps)
self.count_steps += len(rbs)
if obs.last():
R = 0
else:
minimap = np.array(obs.observation['minimap'], dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
action_indices = [
0, 1, 2, 3, 5, 7, 12, 13, 14, 15, 18, 19, 20, 261, 274, 331,
332, 333, 334, 451, 452, 453, 456
]
valid_actions = list(
set(obs.observation['available_actions'])
& set(action_indices))
valid_actions_indices = [
action_indices.index(i) for i in valid_actions
]
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, valid_actions_indices] = 1
feed = {
self.minimap: minimap,
self.screen: screen,
self.info: info
}
R = self.sess.run(self.value, feed_dict=feed)[0]
# Compute targets and masks
minimaps = []
screens = []
infos = []
value_target = np.zeros([len(rbs)], dtype=np.float32)
value_target[-1] = R
valid_spatial_action = np.zeros([len(rbs)], dtype=np.float32)
spatial_action_selected = np.zeros([len(rbs), self.ssize**2],
dtype=np.float32)
valid_non_spatial_action = np.zeros([len(rbs), self.isize],
dtype=np.float32)
non_spatial_action_selected = np.zeros([len(rbs), self.isize],
dtype=np.float32)
rbs.reverse()
for i, [obs, action, next_obs] in enumerate(rbs):
minimap = np.array(obs.observation['minimap'], dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
action_indices = [
0, 1, 2, 3, 5, 7, 12, 13, 14, 15, 18, 19, 20, 261, 274, 331,
332, 333, 334, 451, 452, 453, 456
]
valid_actions = list(
set(obs.observation['available_actions'])
& set(action_indices))
valid_actions_indices = [
action_indices.index(i) for i in valid_actions
]
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, valid_actions_indices] = 1
minimaps.append(minimap)
screens.append(screen)
infos.append(info)
reward = obs.reward
act_id = action.function
act_args = action.arguments
value_target[i] = reward + disc * value_target[i - 1]
# valid_actions = obs.observation["available_actions"]
valid_non_spatial_action[i, valid_actions_indices] = 1
non_spatial_action_selected[i, action_indices.index(act_id)] = 1
args = actions.FUNCTIONS[act_id].args
for arg, act_arg in zip(args, act_args):
if arg.name in ('screen', 'minimap', 'screen2'):
ind = act_arg[1] * self.ssize + act_arg[0]
valid_spatial_action[i] = 1
spatial_action_selected[i, ind] = 1
minimaps = np.concatenate(minimaps, axis=0)
screens = np.concatenate(screens, axis=0)
infos = np.concatenate(infos, axis=0)
# Train
feed = {
self.minimap: minimaps,
self.screen: screens,
self.info: infos,
self.value_target: value_target,
self.valid_spatial_action: valid_spatial_action,
self.spatial_action_selected: spatial_action_selected,
self.valid_non_spatial_action: valid_non_spatial_action,
self.non_spatial_action_selected: non_spatial_action_selected,
self.learning_rate: lr,
self.train_score: train_score
}
_, summary = self.sess.run([self.train_op, self.summary_op],
feed_dict=feed)
self.summary_writer.add_summary(summary, cter)
def update(self, replay_buffer, gamma, learning_rate, step):
obs = replay_buffer[-1][-1]
if obs.last():
reward = 0
else:
screen = np.array(obs.observation.feature_screen, dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
minimap = np.array(obs.observation.feature_minimap,
dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
structured = np.zeros([1, self.structured_dimensions],
dtype=np.float32)
structured[0, obs.observation.available_actions] = 1
feed_dict = {
self.screen_ph: screen,
self.minimap_ph: minimap,
self.structured_ph: structured
}
reward = self.sess.run(self.value, feed_dict=feed_dict)
# compute targets and masks
screens, minimaps, structureds = [], [], []
target_value = np.zeros([len(replay_buffer)], dtype=np.float32)
target_value[-1] = reward
valid_non_spatial_action = np.zeros(
[len(replay_buffer), len(actions.FUNCTIONS)], dtype=np.float32)
sample_non_spatial_action = np.zeros(
[len(replay_buffer), len(actions.FUNCTIONS)], dtype=np.float32)
valid_spatial_action = np.zeros([len(replay_buffer)], dtype=np.float32)
sample_spatial_action = np.zeros(
[len(replay_buffer), self.screen_dimensions**2], dtype=np.float32)
replay_buffer.reverse()
for i, [obs, action, next_obs] in enumerate(replay_buffer):
screen = np.array(obs.observation.feature_screen, dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
minimap = np.array(obs.observation.feature_minimap,
dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
structured = np.zeros([1, self.structured_dimensions],
dtype=np.float32)
structured[0, obs.observation.available_actions] = 1
screens.append(screen)
minimaps.append(minimap)
structureds.append(structured)
reward = obs.reward
action_id = action.function
action_args = action.arguments
target_value[i] = reward + gamma * target_value[i - 1]
available_actions = obs.observation.available_actions
valid_non_spatial_action[i, available_actions] = 1
sample_non_spatial_action[i, action_id] = 1
args = actions.FUNCTIONS[action_id].args
for arg, action_arg in zip(args, action_args):
if arg.name in ('screen', 'minimap', 'screen2'):
spatial_action = action_arg[
1] * self.screen_dimensions + action_arg[0]
valid_spatial_action[i] = 1
sample_spatial_action[i, spatial_action] = 1
screens = np.concatenate(screens, axis=0)
minimaps = np.concatenate(minimaps, axis=0)
structureds = np.concatenate(structureds, axis=0)
feed_dict = {
self.screen_ph: screens,
self.minimap_ph: minimaps,
self.structured_ph: structureds,
self.target_value_ph: target_value,
self.valid_non_spatial_action_ph: valid_non_spatial_action,
self.sample_non_spatial_action_ph: sample_non_spatial_action,
self.valid_spatial_action_ph: valid_spatial_action,
self.sample_spatial_action_ph: sample_spatial_action,
self.learning_rate_ph: learning_rate
}
_, summary = self.sess.run([self.train_op, self.summary_op],
feed_dict=feed_dict)
self.summary_writer.add_summary(summary, step)
def learn(self):
"""when certain number of replay size reach, learn from minibatch replay"""
print("\nstart learning...")
# replace target net parameters
if self.learn_step_counter % self.replace_target_iter == 0:
self.sess.run(self.replace_target_op)
print('\nreplaced target net parameters...')
# sample mini-batch
sample_indices = np.random.choice(len(self.memory), size=self.batch_size)
batch_memory = deque(list(np.array(self.memory)[sample_indices]))
print("selecting minibatch of size: {}..." .format(len(batch_memory)))
# extract s = [], a = [], s' = [], r = []
screens = []
screens_next = []
infos = []
infos_next = []
rewards = []
# actions
valid_spatial_action = np.zeros([self.batch_size], dtype=np.float32)
spatial_action_selected = np.zeros([self.batch_size, self.ssize ** 2], dtype=np.float32)
valid_non_spatial_action = np.zeros([self.batch_size, len(actions.FUNCTIONS)], dtype=np.float32)
non_spatial_action_selected = np.zeros([self.batch_size, len(actions.FUNCTIONS)], dtype=np.float32)
for i, [obs, a, r, obs_] in enumerate(batch_memory):
# s current state from obs
screen = np.array(obs.observation.feature_screen, dtype=np.float32)
screen = np.expand_dims(preprocess_screen(screen), axis=0) # return (bs=1, channel=42, h=64, w=64)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
# s_ next state from obs_
screen_next = np.array(obs_.observation.feature_screen, dtype=np.float32)
screen_next = np.expand_dims(preprocess_screen(screen_next), axis=0) # return (bs=1, channel=42, h=64, w=64)
info_next = np.zeros([1, self.isize], dtype=np.float32)
info_next[0, obs_.observation['available_actions']] = 1
# append to s list, s_ list
screens.append(screen)
infos.append(info)
screens_next.append(screen_next)
infos_next.append(info_next)
# get reward r
rewards.append(r)
# get action 'a'
act_id = a.function
act_args = a.arguments
valid_actions = obs.observation["available_actions"]
valid_non_spatial_action[i, valid_actions] = 1
non_spatial_action_selected[i, act_id] = 1
args = actions.FUNCTIONS[act_id].args
for arg, act_arg in zip(args, act_args):
if arg.name in ('screen', 'minimap', 'screen2'):
ind = act_arg[1] * self.ssize + act_arg[0]
valid_spatial_action[i] = 1
spatial_action_selected[i, ind] = 1
screens = np.concatenate(screens, axis=0) # (32, size of s)
infos = np.concatenate(infos, axis=0)
screens_next = np.concatenate(screens_next, axis=0)
infos_next = np.concatenate(infos_next, axis=0)
rewards = np.transpose(np.array(rewards)) # (32, r)
# get q_next = Q(s', a': theta) to calculate y
q_next = self.sess.run(self.q_next, feed_dict={self.screen: screens_next, self.info: infos_next})
# q_next = self.sess.run(self.q_eval, feed_dict={self.screen: screens_next, self.info: infos_next})
q_target = rewards + self.gamma * q_next
# train
feed = {self.screen: screens,
self.info: infos,
self.q_target: q_target,
self.valid_spatial_action: valid_spatial_action,
self.spatial_action_selected: spatial_action_selected,
self.valid_non_spatial_action: valid_non_spatial_action,
self.non_spatial_action_selected: non_spatial_action_selected
}
# _, summary = self.sess.run([self.train_op, self.summary_op], feed_dict=feed)
# self.summary_writer.add_summary(summary, self.learn_step_counter)
_ = self.sess.run(self.train_op, feed_dict=feed)
self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max
self.learn_step_counter += 1
pass
def update(self, rbs, disc, lr, cter):
# Compute R, which is value of the last observation
obs = rbs[-1][-1]
if obs.last():
R = 0
else:
minimap = np.array(obs.observation['minimap'], dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
feed = {self.minimap: minimap,
self.screen: screen,
self.info: info}
R = self.sess.run(self.value, feed_dict=feed)[0]
# Compute targets and masks
minimaps = []
screens = []
infos = []
value_target = np.zeros([len(rbs)], dtype=np.float32)
value_target[-1] = R
valid_spatial_action = np.zeros([len(rbs)], dtype=np.float32)
spatial_action_selected = np.zeros([len(rbs), self.ssize**2], dtype=np.float32)
valid_non_spatial_action = np.zeros([len(rbs), len(actions.FUNCTIONS)], dtype=np.float32)
non_spatial_action_selected = np.zeros([len(rbs), len(actions.FUNCTIONS)], dtype=np.float32)
rbs.reverse()
for i, [obs, action, next_obs] in enumerate(rbs):
minimap = np.array(obs.observation['minimap'], dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
minimaps.append(minimap)
screens.append(screen)
infos.append(info)
reward = obs.reward
act_id = action.function
act_args = action.arguments
value_target[i] = reward + disc * value_target[i-1]
valid_actions = obs.observation["available_actions"]
valid_non_spatial_action[i, valid_actions] = 1
non_spatial_action_selected[i, act_id] = 1
args = actions.FUNCTIONS[act_id].args
for arg, act_arg in zip(args, act_args):
if arg.name in ('screen', 'minimap', 'screen2'):
ind = act_arg[1] * self.ssize + act_arg[0]
valid_spatial_action[i] = 1
spatial_action_selected[i, ind] = 1
minimaps = np.concatenate(minimaps, axis=0)
screens = np.concatenate(screens, axis=0)
infos = np.concatenate(infos, axis=0)
# Train
feed = {self.minimap: minimaps,
self.screen: screens,
self.info: infos,
self.value_target: value_target,
self.valid_spatial_action: valid_spatial_action,
self.spatial_action_selected: spatial_action_selected,
self.valid_non_spatial_action: valid_non_spatial_action,
self.non_spatial_action_selected: non_spatial_action_selected,
self.learning_rate: lr}
_, summary = self.sess.run([self.train_op, self.summary_op], feed_dict=feed)
self.summary_writer.add_summary(summary, cter)
def step(self, obs): # action selection is in here
minimap = np.array(obs.observation['feature_minimap'],
dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['feature_screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
# TODO: only use available actions
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
feed = {self.minimap: minimap, self.screen: screen, self.info: info}
# Select an action and a spatial target.
valid_actions = np.zeros(self.isize, dtype=np.int32)
valid_actions[obs.observation['available_actions']] = 1
function_id_policy, spatial_policy = self.sess.run(
[self.non_spatial_policy, self.spatial_policy], feed_dict=feed)
# self.logger.info(f"spatial_policy unraveled: {spatial_policy}.")
# self.logger.info(f":{spatial_policy.shape}.")
function_id_policy = function_id_policy.ravel(
) # .ravel flattens the input into 1D array
spatial_policy = spatial_policy.ravel()
# self.logger.info(f"spatial_policy .raveled: {spatial_policy}") # this will help with target below
# self.logger.info(f":{spatial_policy.shape}.")
function_id_policy *= valid_actions
function_ids = np.arange(len(function_id_policy))
function_id_policy /= np.sum(function_id_policy)
# act_id = valid_actions[np.argmax(non_spatial_policy[valid_actions])]
act_id = np.random.choice(function_ids,
p=np.squeeze(function_id_policy))
target = np.argmax(spatial_policy) # currentl will be (,1024)
target = [int(target // self.ssize),
int(target % self.ssize)] # not sure why different operators
if False:
self.logger.info(
f"if false: {actions.FUNCTIONS[act_id].name, target}")
# Epsilon greedy exploration. Keeping this to see if it works
# basically, if eps greedy: take the target and move it left/right and up/down 4 px
if self.training and np.random.rand() < self.epsilon[1]:
dy = np.random.randint(-4, 5)
target[0] = int(max(
0, min(self.ssize - 1, target[0] +
dy))) # make sure target is within possible pxl range
dx = np.random.randint(-4, 5)
target[1] = int(max(0, min(self.ssize - 1, target[1] + dx)))
args = []
# args: A list of the types of args passed to function_type
for arg in actions.FUNCTIONS[act_id].args:
if arg.name in ('screen', 'minimap', 'screen2'):
# x_policy = self.sess.run(
# self.argument_policy[str(arg) + "x"],
# feed_dict=feed)
# y_policy = self.sess.run(
# self.argument_policy[str(arg) + "y"],
# feed_dict=feed)
# x_policy = np.squeeze(x_policy)
# x_ids = np.arange(len(x_policy))
# x = np.random.choice(x_ids, p=x_policy)
# y_policy = np.squeeze(y_policy)
# y_ids = np.arange(len(y_policy))
# y = np.random.choice(y_ids, p=y_policy)
# args.append([x, y])
args.append([target[1], target[0]])
# self.logger.info(f"target coords: {[target[1], target[0]]}")
else:
arg_policy = self.sess.run(self.argument_policy[str(arg)],
feed_dict=feed)
arg_policy = np.squeeze(arg_policy)
arg_ids = np.arange(len(arg_policy))
arg_index = np.random.choice(arg_ids, p=arg_policy)
args.append([arg_index])
# self.logger.info(f"arg: index: {arg_index}")
# args.append([0])
# sizes: The max+1 of each of the dimensions this argument takes.
return actions.FunctionCall(
act_id, args) # args should be int from (0, arg.size)
def step(self, obs):
super(RandomAgent, self).step(obs)
self.randomOrgreedy = False
feature_screen = np.expand_dims(preprocess_screen(
obs.observation.feature_screen),
axis=0)
feature_map = np.expand_dims(preprocess_minimap(
obs.observation.feature_minimap),
axis=0)
info = np.zeros([1, self.action_size], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
feed_dict = {
self.minimap: feature_map,
self.screen: feature_screen,
self.info: info
}
non_spatial_action, spatial_action = self.sess.run(
[self.non_spatial_action, self.spatial_action],
feed_dict=feed_dict)
non_spatial_action = non_spatial_action.ravel()
spatial_action = spatial_action.ravel() #output shape 4096
target = np.argmax(spatial_action)
target = [
int(target // self.minimap_size),
int(target % self.minimap_size)
]
valid_actions = obs.observation.available_actions
act_id = valid_actions[np.argmax(non_spatial_action[valid_actions])]
# print("available actions = " + str(obs.observation.available_actions))
# function_id = numpy.random.choice(obs.observation.available_actions)
# function_id = 1
# print("function_id = " + str(function_id))
# print("observation_spec " + str(self.obs_spec))
# print("action_spec" + str((self.action_spec.functions)))
# args = [[numpy.random.randint(0, size) for size in arg.sizes]
# for arg in self.action_spec.functions[function_id].args]
# print("function args = " + str(self.action_spec.functions[function_id].args))
# for id in obs.observation.available_actions:
# for arg in self.action_spec.functions[id].args:
# ctr = 0
# for size in arg.sizes:
# ctr +=1
# if(ctr>2):
# print("function_id = " + str(id))
if np.random.rand() < self.epsilon[0]:
act_id = np.random.choice(valid_actions)
self.randomOrgreedy = True
if np.random.rand() < self.epsilon[1]:
dy = np.random.randint(-4, 5)
target[0] = int(max(0, min(self.screen_size - 1, target[0] + dy)))
dx = np.random.randint(-4, 5)
target[1] = int(max(0, min(self.screen_size - 1, target[1] + dx)))
act_args = []
for arg in self.action_spec.functions[act_id].args:
if arg.name in ('screen', 'minimap', 'screen2'):
act_args.append([target[1], target[0]])
else:
act_args.append([0]) # TODO: Be careful
if (act_id != self.temp_act_id):
self.temp_act_id = act_id
if (self.randomOrgreedy):
print("RANDOM")
print("action " + str(actions.FUNCTIONS[act_id].name))
print("target" + str(target))
# print("args = " + str(args))
# print("\n\n\n")
return actions.FunctionCall(act_id, act_args)
def update(self, rbs, disc, lr, cter):
# Compute R, which is value of the last observation
obs = rbs[-1][-1]
if obs.last():
# obs[3]['score_cumulative'][0] or obs.reward
R = obs[3]['score_cumulative'][0]
# enums from https://github.com/Blizzard/s2client-api/blob/master/include/sc2api/sc2_typeenums.h
_TERRAN_BARRACKS = 21
_TERRAN_MARINE = 48
_UNIT_TYPE = features.SCREEN_FEATURES.unit_type.index
unit_type = obs.observation['feature_screen'][_UNIT_TYPE]
barracks_y, barracks_x = (unit_type == _TERRAN_BARRACKS).nonzero()
if barracks_x.any():
print('Barracks detected')
R += 1
print('Episode reward: {}'.format(R))
else:
minimap = np.array(obs.observation['feature_minimap'],
dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['feature_screen'],
dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
feed = {
self.minimap: minimap,
self.screen: screen,
self.info: info
}
R = self.sess.run(self.value, feed_dict=feed)[0]
# Compute targets and masks
minimaps = []
screens = []
infos = []
value_target = np.zeros([len(rbs)], dtype=np.float32)
value_target[-1] = R
valid_spatial_action = np.zeros([len(rbs)], dtype=np.float32)
spatial_action_selected = np.zeros([len(rbs), self.ssize**2],
dtype=np.float32)
valid_non_spatial_action = np.zeros(
[len(rbs), len(actions.FUNCTIONS)], dtype=np.float32)
non_spatial_action_selected = np.zeros(
[len(rbs), len(actions.FUNCTIONS)], dtype=np.float32)
rbs.reverse()
for i, [obs, action, next_obs] in enumerate(rbs):
minimap = np.array(obs.observation['feature_minimap'],
dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['feature_screen'],
dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
minimaps.append(minimap)
screens.append(screen)
infos.append(info)
#reward = obs.reward
reward = 0.25 * (next_obs.observation['score_cumulative'][0] -
obs.observation['score_cumulative'][0])
act_id = action.function
act_args = action.arguments
value_target[i] = reward + disc * value_target[i - 1]
valid_actions = obs.observation["available_actions"]
valid_non_spatial_action[i, valid_actions] = 1
non_spatial_action_selected[i, act_id] = 1
args = actions.FUNCTIONS[act_id].args
for arg, act_arg in zip(args, act_args):
if arg.name in ('screen', 'minimap', 'screen2'):
ind = act_arg[1] * self.ssize + act_arg[0]
valid_spatial_action[i] = 1
spatial_action_selected[i, ind] = 1
minimaps = np.concatenate(minimaps, axis=0)
screens = np.concatenate(screens, axis=0)
infos = np.concatenate(infos, axis=0)
for minimaps, screens, infos, value_target, valid_spatial_action, spatial_action_selected, valid_non_spatial_action, \
non_spatial_action_selected in zip(*[self.batch(mask, BATCH_SIZE) for mask in [minimaps, screens, infos, value_target, valid_spatial_action,
spatial_action_selected, valid_non_spatial_action, non_spatial_action_selected]]):
# Train in batches
feed = {
self.minimap: minimaps,
self.screen: screens,
self.info: infos,
self.value_target: value_target,
self.valid_spatial_action: valid_spatial_action,
self.spatial_action_selected: spatial_action_selected,
self.valid_non_spatial_action: valid_non_spatial_action,
self.non_spatial_action_selected: non_spatial_action_selected,
self.learning_rate: lr
}
#print('Commiting {} replay samples'.format(len(minimaps)))
_, summary = self.sess.run([self.train_op, self.summary_op],
feed_dict=feed)
self.summary_writer.add_summary(summary, cter)
def step(self, obs, use_unit_selector):
minimap = np.array(obs.observation['minimap'], dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
if self.init_counter == 0:
self.init_counter += 1
return actions.FunctionCall(7, [[1]])
elif self.init_counter == 1:
self.init_counter += 1
return actions.FunctionCall(
5, [[sc_ui.ActionMultiPanel.SingleSelect], [0]])
elif self.init_counter == 2:
self.init_counter += 1
return actions.FunctionCall(4, [[1], [0]])
elif self.init_counter == 3:
self.init_counter += 1
return actions.FunctionCall(7, [[1]])
elif self.init_counter == 4:
self.init_counter += 1
return actions.FunctionCall(
5, [[sc_ui.ActionMultiPanel.SingleSelect], [1]])
elif self.init_counter == 5:
self.init_counter += 1
return actions.FunctionCall(4, [[1], [1]])
elif use_unit_selector:
unitSel = self.get_unit_sel_res(obs)
if self.training and np.random.rand() < self.epsilon[0]:
unitSel = np.random.randint(0, 4)
if unitSel == num_units + 1:
return actions.FunctionCall(7, [[1]])
elif unitSel == num_units:
feed = {
self.minimap: minimap,
self.screen: screen,
self.info: info
}
non_spatial_action, spatial_action = self.sess_master.run(
[self.non_spatial_action, self.spatial_action],
feed_dict=feed)
# Select an action and a spatial target
non_spatial_action = non_spatial_action.ravel()
spatial_action = spatial_action.ravel()
valid_actions = obs.observation['available_actions']
act_id = valid_actions[np.argmax(
non_spatial_action[valid_actions])]
target = np.argmax(spatial_action)
target = [int(target // self.ssize), int(target % self.ssize)]
# Epsilon greedy exploration
if self.training and np.random.rand() < self.epsilon[0]:
act_id = np.random.choice(valid_actions)
if self.training and np.random.rand() < self.epsilon[1]:
dy = np.random.randint(-4, 5)
target[0] = int(max(0, min(self.ssize - 1,
target[0] + dy)))
dx = np.random.randint(-4, 5)
target[1] = int(max(0, min(self.ssize - 1,
target[1] + dx)))
# Set act_id and act_args
act_args = []
for arg in actions.FUNCTIONS[act_id].args:
if arg.name in ('screen', 'minimap', 'screen2'):
act_args.append([target[1], target[0]])
else:
act_args.append([0]) # TODO: Be careful
return actions.FunctionCall(act_id, act_args)
else:
return actions.FunctionCall(4, [[0], [unitSel]])
else:
feed = {
self.minimap: minimap,
self.screen: screen,
self.info: info
}
non_spatial_action, spatial_action = self.sess_master.run(
[self.non_spatial_action, self.spatial_action], feed_dict=feed)
# Select an action and a spatial target
non_spatial_action = non_spatial_action.ravel()
spatial_action = spatial_action.ravel()
valid_actions = obs.observation['available_actions']
act_id = valid_actions[np.argmax(
non_spatial_action[valid_actions])]
target = np.argmax(spatial_action)
target = [int(target // self.ssize), int(target % self.ssize)]
# Epsilon greedy exploration
if self.training and np.random.rand() < self.epsilon[0]:
act_id = np.random.choice(valid_actions)
if self.training and np.random.rand() < self.epsilon[1]:
dy = np.random.randint(-4, 5)
target[0] = int(max(0, min(self.ssize - 1, target[0] + dy)))
dx = np.random.randint(-4, 5)
target[1] = int(max(0, min(self.ssize - 1, target[1] + dx)))
# Set act_id and act_args
act_args = []
for arg in actions.FUNCTIONS[act_id].args:
if arg.name in ('screen', 'minimap', 'screen2'):
act_args.append([target[1], target[0]])
else:
act_args.append([0]) # TODO: Be careful
return actions.FunctionCall(act_id, act_args)
def update_low(self, ind_thread, rbs, dhs, disc, lr_a, lr_c, cter,
macro_type, coord_type):
# rbs(replayBuffers)是[last_timesteps[0], actions[0], timesteps[0]]的集合（agent在一回合里进行了多少step就有多少个），具体见run_loop25行
# Compute R, which is value of the last observation
obs = rbs[-1][-1] # rbs的最后一个元素，应当是当前一步的timesteps值。即obs可以看作timesteps
if obs.last():
R = 0
else:
minimap = np.array(obs.observation['feature_minimap'],
dtype=np.float32) # 类似105-111行
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['feature_screen'],
dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
info_plus = np.zeros([1, self.info_plus_size], dtype=np.float32)
info_plus[0] = obs.observation.player.minerals, obs.observation[
'player'][5], obs.observation['player'][6], obs.observation[
'player'][4]
# info 现在的size 是 isize + info_plus_size
info = np.concatenate((info, info_plus), axis=1)
# print('info')
# print(info)
# print(info_plus)
dir_high_usedToFeedLowNet = np.ones([1, 1], dtype=np.float32)
dir_high_usedToFeedLowNet[0][0] = dhs[0]
act_id = np.ones([1, 1], dtype=np.float32)
# act_ID[0][0] = rbs[-1][1].function
# 之所以不能用rbs里的action信息，是因为rbs里的action可能是no_op(由于出现动作not valid/不合法的情况，为了使游戏不崩掉而不得不这么办的补救措施)
# 但这里要输入的act_id应该是step_low算出来的act_id
act_id[0][0] = GL.get_value(ind_thread, "act_id_micro")
feed = {
self.minimap: minimap,
self.screen: screen,
self.info: info,
self.dir_high_usedToFeedLowNet: dir_high_usedToFeedLowNet,
self.act_id: act_id,
}
R = self.sess.run(self.value_low, feed_dict=feed)[0]
# Compute targets and masks
minimaps = []
screens = []
infos = []
dir_highs = []
act_ids = []
value_target = np.zeros(
[len(rbs)], dtype=np.float32) # len(rbs) 计算出agent在回合里总共进行的步数
value_target[-1] = R
valid_spatial_action = np.zeros([len(rbs)],
dtype=np.float32) # 含义是每一个step需不需要坐标参数
spatial_action_selected = np.zeros(
[len(rbs), self.ssize**2],
dtype=np.float32) # 含义是每一个step需不需要坐标参数（第一维上），且具体坐标参数是什么（第二维上）
rbs.reverse() # 先reverse 与莫烦A3C_continuous_action.py的代码类似
micro_isdone = GL.get_value(ind_thread, "micro_isdone")
micro_isdone.reverse()
sum_low_reward = GL.get_value(ind_thread, "sum_low_reward")
for i, [obs, action,
next_obs] in enumerate(rbs): # agent在回合里进行了多少步，就进行多少轮循环
minimap = np.array(obs.observation['feature_minimap'],
dtype=np.float32) # 类似105-111行
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['feature_screen'],
dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
info_plus = np.zeros([1, self.info_plus_size], dtype=np.float32)
info_plus[0] = obs.observation.player.minerals, obs.observation[
'player'][5], obs.observation['player'][6], obs.observation[
'player'][4]
# info 现在的size 是 isize + info_plus_size
info = np.concatenate((info, info_plus), axis=1)
minimaps.append(minimap)
screens.append(screen)
infos.append(info)
dir_high_usedToFeedLowNet = np.ones([1, 1], dtype=np.float32)
dir_high_usedToFeedLowNet[0][0] = dhs[i]
act_ID = np.ones([1, 1], dtype=np.float32)
# act_ID[0][0] = act_id
# 之所以不能用rbs里的action信息，是因为rbs里的action可能是no_op(由于出现动作not valid/不合法的情况，为了使游戏不崩掉而不得不这么办的补救措施)
# 但这里要输入的act_id应该是step_low算出来的act_id
act_ID[0][0] = GL.get_value(ind_thread, "act_id_micro")
# dir_highs.append(dir_high_usedToFeedLowNet)
# act_ids.append(act_ID)
coord = [0, 0]
# coord[0], coord[1] = [32, 32]
coord[0], coord[1] = self.step_low(ind_thread, obs,
dir_high_usedToFeedLowNet,
act_ID)
reward = low_reward(next_obs, obs, coord, micro_isdone[i],
macro_type, coord_type)
sum_low_reward += reward
GL.add_value_list(ind_thread, "low_reward_of_episode", reward)
act_id = action.function # Agent在这一步中选择动作的id序号
act_args = action.arguments
value_target[i] = reward + disc * value_target[
i -
1] # 可参考莫烦Q_Learning教程中对Gamma的意义理解的那张图（有3个眼镜那张），得到回合中每个状态的价值V_S
# 这里没像莫烦一样再次reverse value 似乎是因为其他参数（如minimap、screen、info等）也都是最后往前反序排列的。见181-182行
args = actions.FUNCTIONS[act_id].args
for arg, act_arg in zip(args, act_args):
if arg.name in ('screen', 'minimap', 'screen2'):
ind = act_arg[1] * self.ssize + act_arg[0]
valid_spatial_action[i] = 1
spatial_action_selected[i, ind] = 1
GL.set_value(ind_thread, "sum_low_reward", sum_low_reward)
minimaps = np.concatenate(minimaps, axis=0)
screens = np.concatenate(screens, axis=0)
infos = np.concatenate(infos, axis=0)
# 实际上由于low_net是单步更新策略，所以以下feed的参数里面都只有一帧的数据
# Train
feed = {
self.minimap:
minimaps,
self.screen:
screens,
self.info:
infos,
# self.dir_high_usedToFeedLowNet: dir_highs,
self.dir_high_usedToFeedLowNet:
dir_high_usedToFeedLowNet,
# self.act_id: act_ids,
self.act_id:
act_ID,
self.value_target_low:
value_target,
self.valid_spatial_action_low:
valid_spatial_action,
self.spatial_action_selected_low:
spatial_action_selected,
self.learning_rate_a_low:
lr_a,
self.learning_rate_c_low:
lr_c
}
_, __, summary = self.sess.run(
[self.update_a_low, self.update_c_low, self.summary_op_low],
feed_dict=feed)
self.summary_writer.add_summary(summary, cter)
def update(self, rbs, disc, lr, cter):
# Compute R, which is value of the last observation
spatial_action = None
non_spatial_action = None
obs = rbs[-1][-1]
if obs.last():
R = 0
else:
minimap = np.array(obs.observation.feature_minimap, dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation.feature_screen, dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation.available_actions] = 1
# first get probabilities for each action; Then greedly pick the largest to calculate q value. one hot vector softmax
# have low confidence, just use full episode and the last observation R should be just 0.
feed = {self.minimap: minimap,
self.screen: screen,
self.info: info}
spatial_action, non_spatial_action = self.sess.run([self.spatial_action, self.non_spatial_action], feed_dict=feed)
# Compute targets and masks
minimaps = []
screens = []
infos = []
valid_spatial_action = np.zeros([len(rbs)], dtype=np.float32)
spatial_action_selected = np.zeros([len(rbs), self.ssize**2], dtype=np.float32)
valid_non_spatial_action = np.zeros([len(rbs), len(actions.FUNCTIONS)], dtype=np.float32)
non_spatial_action_selected = np.zeros([len(rbs), len(actions.FUNCTIONS)], dtype=np.float32)
rbs.reverse()
for i, [obs, action, next_obs] in enumerate(rbs):
minimap = np.array(obs.observation.feature_minimap, dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation.feature_screen, dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
#info[0, obs.observation['available_actions']] = 1
info[0, obs.observation.available_actions] = 1
minimaps.append(minimap)
screens.append(screen)
infos.append(info)
act_id = action.function
act_args = action.arguments
#valid_actions = obs.observation["available_actions"]
valid_actions = obs.observation.available_actions
valid_non_spatial_action[i, valid_actions] = 1
non_spatial_action_selected[i, act_id] = 1
args = actions.FUNCTIONS[act_id].args
for arg, act_arg in zip(args, act_args):
if arg.name in ('screen', 'minimap', 'screen2'):
ind = act_arg[1] * self.ssize + act_arg[0]
valid_spatial_action[i] = 1
spatial_action_selected[i, ind] = 1
value_target = np.zeros([len(rbs)], dtype=np.float32)
if spatial_action is not None:
q_spatial = np.max(spatial_action * valid_spatial_action[0], axis=1)
q_non_spatial = np.max(non_spatial_action * valid_non_spatial_action[0], axis=1)
q_value = self.ispatial*q_spatial + q_non_spatial
R = q_value[0]
value_target[-1] = R
for i, [obs, action, next_obs] in enumerate(rbs):
reward = obs.reward
value_target[i] = reward + disc * value_target[i-1]
minimaps = np.concatenate(minimaps, axis=0)
screens = np.concatenate(screens, axis=0)
infos = np.concatenate(infos, axis=0)
# Train
feed = {self.minimap: minimaps,
self.screen: screens,
self.info: infos,
self.value_target: value_target,
self.valid_spatial_action: valid_spatial_action,
self.spatial_action_selected: spatial_action_selected,
self.valid_non_spatial_action: valid_non_spatial_action,
self.non_spatial_action_selected: non_spatial_action_selected,
self.learning_rate: lr}
_, summary = self.sess.run([self.train_op, self.summary_op], feed_dict=feed)
self.summary_writer.add_summary(summary, cter)
def update_high(self, ind_thread, rbs, dhs, disc, lr_a, lr_c, cter):
# rbs(replayBuffers)是[last_timesteps[0], actions[0], timesteps[0]]的集合（更新时经历了多少个step就有多少个），具体见run_loop25行
# dhs(dir_high_buffers) 是指令序号的集合。比如一共有5个宏动作，则dhs形如[5, 4, 1, 2, 3, 4, 2, 1, ......]
dir_high_selected = np.zeros(
[len(rbs), num_macro_action],
dtype=np.float32) # 含义是每一个step需不需要坐标参数（第一维上），且具体坐标参数是什么（第二维上）
for i in range(len(rbs)):
dir_high_selected[i, dhs[i][0] - 1] = 1
# Compute R, which is value of the last observation
obs = rbs[-1][-1] # rbs的最后一个元素，应当是当前一步的timesteps值。即obs可以看作timesteps
if obs.last():
R = 0
else:
minimap = np.array(obs.observation['feature_minimap'],
dtype=np.float32) # 类似105-111行
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['feature_screen'],
dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
info_plus = np.zeros([1, self.info_plus_size], dtype=np.float32)
info_plus[0] = obs.observation.player.minerals, obs.observation[
'player'][5], obs.observation['player'][6], obs.observation[
'player'][4]
# info 现在的size 是 isize + info_plus_size
info = np.concatenate((info, info_plus), axis=1)
feed = {
self.minimap: minimap,
self.screen: screen,
self.info: info
}
R = self.sess.run(self.value_high, feed_dict=feed)[0]
# Compute targets and masks
minimaps = []
screens = []
infos = []
value_target = np.zeros(
[len(rbs)], dtype=np.float32) # len(rbs) 计算出agent在回合里总共进行的步数
value_target[-1] = R
valid_spatial_action = np.zeros([len(rbs)],
dtype=np.float32) # 含义是每一个step需不需要坐标参数
spatial_action_selected = np.zeros(
[len(rbs), self.ssize**2],
dtype=np.float32) # 含义是每一个step需不需要坐标参数（第一维上），且具体坐标参数是什么（第二维上）
rbs.reverse() # 先reverse 与莫烦A3C_continuous_action.py的代码类似
micro_isdone = GL.get_value(ind_thread, "micro_isdone")
micro_isdone.reverse()
sum_high_reward = GL.get_value(ind_thread, "sum_high_reward")
for i, [obs, action,
next_obs] in enumerate(rbs): # agent在回合里进行了多少步，就进行多少轮循环
minimap = np.array(obs.observation['feature_minimap'],
dtype=np.float32) # 类似105-111行
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['feature_screen'],
dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
info_plus = np.zeros([1, self.info_plus_size], dtype=np.float32)
info_plus[0] = obs.observation.player.minerals, obs.observation[
'player'][5], obs.observation['player'][6], obs.observation[
'player'][4]
# info 现在的size 是 isize + info_plus_size
info = np.concatenate((info, info_plus), axis=1)
minimaps.append(minimap)
screens.append(screen)
infos.append(info)
# reward = obs.reward
reward = high_reward(ind_thread, next_obs, obs, action,
micro_isdone[i]) # 翔森设计的high reward
sum_high_reward += reward
GL.add_value_list(ind_thread, "high_reward_of_episode", reward)
act_id = action.function # Agent在这一步中选择动作的id序号
act_args = action.arguments
value_target[i] = reward + disc * value_target[
i -
1] # 可参考莫烦Q_Learning教程中对Gamma的意义理解的那张图（有3个眼镜那张），得到回合中每个状态的价值V_S
# 这里没像莫烦一样再次reverse value 似乎是因为其他参数（如minimap、screen、info等）也都是最后往前反序排列的。见181-182行
args = actions.FUNCTIONS[act_id].args
for arg, act_arg in zip(args, act_args):
if arg.name in ('screen', 'minimap', 'screen2'):
ind = act_arg[1] * self.ssize + act_arg[0]
valid_spatial_action[i] = 1
spatial_action_selected[i, ind] = 1
GL.set_value(ind_thread, "sum_high_reward", sum_high_reward)
minimaps = np.concatenate(minimaps, axis=0)
screens = np.concatenate(screens, axis=0)
infos = np.concatenate(infos, axis=0)
# Train
feed = {
self.minimap: minimaps,
self.screen: screens,
self.info: infos,
self.value_target_high: value_target,
self.dir_high_selected: dir_high_selected,
self.learning_rate_a_high: lr_a,
self.learning_rate_c_high: lr_c
}
_, __, summary = self.sess.run(
[self.update_a_high, self.update_c_high, self.summary_op_high],
feed_dict=feed)
self.summary_writer.add_summary(summary, cter)
GL.set_value(ind_thread, "micro_isdone", [])
def step(self, obs):
screen = np.array(obs.observation.feature_screen, dtype=np.float32)
screen = np.expand_dims(utils.preprocess_screen(screen), axis=0)
# np.expand_dims: 展開數組的形狀。
# x = np.array([1,2])
# x.shape
# (2,)
# y = np.expand_dims(x, axis=0)
# y
# array([[1, 2]])
# y.shape
# (1, 2)
minimap = np.array(obs.observation.feature_minimap, dtype=np.float32)
minimap = np.expand_dims(utils.preprocess_minimap(minimap), axis=0)
structured = np.zeros([1, self.structured_dimensions], dtype=np.float32)
structured[0, obs.observation.available_actions] = 1
feed_dict = {
self.screen: screen,
self.minimap: minimap,
self.structured: structured
}
non_spatial_action, spatial_action = self.sess.run(
[self.non_spatial_action, self.spatial_action],
feed_dict=feed_dict
)
non_spatial_action = non_spatial_action.ravel()
spatial_action = spatial_action.ravel()
# np.ravel: 返回一個連續的扁平數組(flatten array)。
# x = np.array([[1, 2, 3], [4, 5, 6]])
# print(np.ravel(x))
# [1 2 3 4 5 6]
available_actions = obs.observation.available_actions
action_id = 0
spatial_target = []
if self.mode == 'original_ac3':
non_spatial_action = np.array(non_spatial_action[available_actions])
non_spatial_action /= non_spatial_action.sum()
x = np.random.choice(non_spatial_action, p=non_spatial_action)
action_id = available_actions[np.where(non_spatial_action == x)[0][0]]
spatial_target = random.choice(list(enumerate(spatial_action)))[0]
# x = np.random.choice(spatial_action, p=spatial_action)
# if len(np.where(spatial_action == x)[0]) > 1:
# random = np.random.choice(len(np.where(spatial_action == x)[0]))
# spatial_target = np.where(spatial_action == x)[0][random]
# else:
# spatial_target = np.where(spatial_action == x)[0][0]
spatial_target = [int(spatial_target // self.resolution), int(spatial_target % self.resolution)]
else:
action_id = available_actions[np.argmax(non_spatial_action[available_actions])]
spatial_target = np.argmax(spatial_action)
spatial_target = [int(spatial_target // self.resolution), int(spatial_target % self.resolution)]
# epsilon-greedy exploration
if self.training and np.random.rand() < self.epsilon[0]:
action_id = np.random.choice(available_actions)
if self.training and np.random.rand() < self.epsilon[1]:
delta_y, delta_x = np.random.randint(-4, 5), np.random.randint(-4, 5)
spatial_target[0] = int(max(0, min(self.resolution -1, spatial_target[0] + delta_y)))
spatial_target[1] = int(max(0, min(self.resolution -1, spatial_target[1] + delta_x)))
action_args = []
for arg in actions.FUNCTIONS[action_id].args:
if arg.name in ('screen', 'minimap', 'screen2'):
action_args.append([spatial_target[1], spatial_target[0]])
else:
action_args.append([0])
return actions.FunctionCall(action_id, action_args)
def step(self, obs):
minimap = np.array(obs.observation['minimap'], dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
# TODO: only use available actions
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
#print('info = ',info)
feed = {self.minimap: minimap, self.screen: screen, self.info: info}
non_spatial_action, spatial_action = self.sess.run(
[self.non_spatial_action, self.spatial_action], feed_dict=feed)
# Select an action and a spatial target
non_spatial_action = non_spatial_action.ravel()
spatial_action = spatial_action.ravel()
valid_actions = obs.observation['available_actions']
#print('valid_actions = ',valid_actions)
#print('self.less_actions = ',self.less_actions)
#找valid_actions中各元素在self.less_actions中的脚标
valid_actions_idx = []
for i in range(len(valid_actions)):
for j in range(len(self.less_actions)):
if (self.less_actions[j] == valid_actions[i]):
valid_actions_idx.append(j)
#valid_actions_idx = np.sort(valid_actions_idx)
act_id = int(self.less_actions[np.argmax(
non_spatial_action[valid_actions_idx])])
#print('valid_actions_idx = ',valid_actions_idx)
#print('np.argmax(non_spatial_action[valid_actions_idx]) = ', np.argmax(non_spatial_action[valid_actions_idx]))
#print('act_id = ',act_id)
target = np.argmax(spatial_action)
target = [int(target // self.ssize), int(target % self.ssize)]
#if False:
# print(actions.FUNCTIONS[act_id].name, target)
# Epsilon greedy exploration
if self.training and np.random.rand() < self.epsilon[0]:
act_id = np.random.choice(valid_actions)
if self.training and np.random.rand() < self.epsilon[1]:
dy = np.random.randint(-4, 5)
target[0] = int(max(0, min(self.ssize - 1, target[0] + dy)))
dx = np.random.randint(-4, 5)
target[1] = int(max(0, min(self.ssize - 1, target[1] + dx)))
# Set act_id and act_args
act_args = []
for arg in actions.FUNCTIONS[act_id].args:
if arg.name in ('screen', 'minimap', 'screen2'):
act_args.append([target[1], target[0]])
else:
act_args.append([0]) # TODO: Be careful
if (not act_id in valid_actions):
return actions.FunctionCall(_NOOP, [])
return actions.FunctionCall(act_id, act_args)
def update(self, replay_buffer, learning_rate, step):
obs = replay_buffer[-1][-1]
if obs.last():
reward = 0
else:
screen = np.array(obs.observation.feature_screen, dtype=np.float32)
screen = np.expand_dims(utils.preprocess_screen(screen), axis=0)
minimap = np.array(obs.observation.feature_minimap, dtype=np.float32)
minimap = np.expand_dims(utils.preprocess_minimap(minimap), axis=0)
structured = np.zeros([1, self.structured_dimensions], dtype=np.float32)
structured[0, obs.observation.available_actions] = 1
feed_dict = {
self.screen: screen,
self.minimap: minimap,
self.structured: structured
}
reward = self.sess.run(self.value, feed_dict=feed_dict)
#compute targets and masks
screens, minimaps, structureds = [], [], []
target_value = np.zeros([len(replay_buffer)], dtype=np.float32)
target_value[-1] = reward
valid_non_spatial_action = np.zeros([len(replay_buffer), len(actions.FUNCTIONS)], dtype=np.float32)
non_spatial_action_selected = np.zeros([len(replay_buffer), len(actions.FUNCTIONS)], dtype=np.float32)
valid_spatial_action = np.zeros([len(replay_buffer)], dtype=np.float32)
spatial_action_selected = np.zeros([len(replay_buffer), self.resolution ** 2], dtype=np.float32)
record_score = replay_buffer[-1][0].observation['score_cumulative'][0]
summary = tf.Summary()
summary.value.add(tag='episode_score', simple_value=record_score)
print('train!! step %d: score = %f' % (step, record_score))
self.summary_writer.add_summary(summary, step)
replay_buffer.reverse()
# reverse:方法沒有返回值，但是會對列表的元素進行反向排序
for i, [obs, action, next_obs] in enumerate(replay_buffer):
# seq = ['one', 'two', 'three']
# for i, element in enumerate(seq):
# print i, element
# 0 one
# 1 two
# 2 three
screen = np.array(obs.observation.feature_screen, dtype=np.float32)
screen = np.expand_dims(utils.preprocess_screen(screen), axis=0)
minimap = np.array(obs.observation.feature_minimap, dtype=np.float32)
minimap = np.expand_dims(utils.preprocess_minimap(minimap), axis=0)
structured = np.zeros([1, self.structured_dimensions], dtype=np.float32)
structured[0, obs.observation.available_actions] = 1
screens.append(screen)
minimaps.append(minimap)
structureds.append(structured)
reward = obs.reward
action_id = action.function
action_args = action.arguments
target_value[i] = reward + self.discount * target_value[i - 1]
available_actions = obs.observation.available_actions
valid_non_spatial_action[i, available_actions] = 1
non_spatial_action_selected[i, action_id] = 1
args = actions.FUNCTIONS[action_id].args
for arg, action_arg in zip(args, action_args):
if arg.name in ('screen', 'minimap', 'screen2'):
spatial_action = action_arg[1] * self.resolution + action_arg[0]
valid_spatial_action[i] = 1
spatial_action_selected[i, spatial_action] = 1
screens = np.concatenate(screens, axis=0)
minimaps = np.concatenate(minimaps, axis=0)
structureds = np.concatenate(structureds, axis=0)
feed_dict = {
self.screen: screens,
self.minimap: minimaps,
self.structured: structureds,
self.target_value: target_value,
self.valid_non_spatial_action: valid_non_spatial_action,
self.non_spatial_action_selected: non_spatial_action_selected,
self.valid_spatial_action: valid_spatial_action,
self.spatial_action_selected: spatial_action_selected,
self.learning_rate: learning_rate
}
_, summary = self.sess.run([self.train_op, self.summary_op], feed_dict=feed_dict)
self.summary_writer.add_summary(summary, step)
def update(self, rbs, replay_buffer, disc, lr, cter):
# Compute R, which is value of the last observation
buffer_size = len(replay_buffer)
obs = rbs[-1][-1]
if obs.last():
R = 0
else:
minimap = np.array(obs.observation['minimap'], dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
feed = {
self.minimap: minimap,
self.screen: screen,
self.info: info
}
R = self.sess.run(self.value, feed_dict=feed)[0]
# Compute targets and masks
minimaps = []
screens = []
infos = []
value_target = np.zeros([len(rbs)], dtype=np.float32)
value_target[-1] = R
valid_spatial_action = np.zeros([len(rbs)], dtype=np.float32)
spatial_action_selected = np.zeros([len(rbs), self.ssize**2],
dtype=np.float32)
valid_non_spatial_action = np.zeros(
[len(rbs), len(actions.FUNCTIONS)], dtype=np.float32)
non_spatial_action_selected = np.zeros(
[len(rbs), len(actions.FUNCTIONS)], dtype=np.float32)
rbs.reverse()
for i, [obs, action, pixel_change, next_obs] in enumerate(rbs):
# added pixel change to update function, just directly put it into the feed dict
minimap = np.array(obs.observation['minimap'], dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
minimaps.append(minimap)
screens.append(screen)
infos.append(info)
reward = obs.reward
act_id = action.function
act_args = action.arguments
value_target[i] = reward + disc * value_target[i - 1]
valid_actions = obs.observation["available_actions"]
valid_non_spatial_action[i, valid_actions] = 1
non_spatial_action_selected[i, act_id] = 1
args = actions.FUNCTIONS[act_id].args
for arg, act_arg in zip(args, act_args):
if arg.name in ('screen', 'minimap', 'screen2'):
ind = act_arg[1] * self.ssize + act_arg[0]
valid_spatial_action[i] = 1
spatial_action_selected[i, ind] = 1
minimaps = np.concatenate(minimaps, axis=0)
screens = np.concatenate(screens, axis=0)
infos = np.concatenate(infos, axis=0)
# Train
feed = {
self.minimap: minimaps,
self.screen: screens,
self.pixel_change: pixel_change,
self.info: infos,
self.value_target: value_target,
self.valid_spatial_action: valid_spatial_action,
self.spatial_action_selected: spatial_action_selected,
self.valid_non_spatial_action: valid_non_spatial_action,
self.non_spatial_action_selected: non_spatial_action_selected,
self.learning_rate: lr
}
# _, summary = self.sess.run([self.train_op, self.summary_op], feed_dict=feed)
# self.summary_writer.add_summary(summary, cter)
######################################################################################
# Update the pc network
start_pos = np.random.randint(0, buffer_size - self.sequence_size - 1)
#take care of terminals
if replay_buffer[start_pos][-1].last():
start_pos += 1
# Assuming that there are no successive terminal frames.
pc_experience_frames = []
for i in range(self.sequence_size + 1):
frame = replay_buffer[start_pos + i]
pc_experience_frames.append(frame)
if frame[-1].last():
break
# Reverse sequence to calculate from the last
pc_experience_frames.reverse()
batch_pc_si = []
batch_pc_a = []
batch_pc_R = []
batch_pc_va = []
pc_R = np.zeros([20, 20], dtype=np.float32)
if not pc_experience_frames[1].last():
# pc_R = self.run_pc_q_max(self.sess, pc_experience_frames[0].state)
# def run_pc_q_max(self, sess, s_t):
minimap = np.array(obs.observation['minimap'], dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['screen'], dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
# TODO: only use available actions
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
s_feed = {
self.pc_minimap: minimap,
self.pc_screen: screen,
self.pc_info: info
}
pc_R = self.sess.run(self.pc_q_max, s_feed)
pc_valid_non_spatial_action = np.zeros(
[len(rbs), len(actions.FUNCTIONS)], dtype=np.float32)
for i, [obs, action, pixel_change,
next_obs] in enumerate(pc_experience_frames[1:]):
pc_R = pixel_change + self.gamma_pc * pc_R
a = np.zeros([self.action_size])
a[action] = 1.0
valid_actions = np.zeros((len(actions.FUNCTIONS)),
dtype=np.float32)
valid_actions_inds = obs.observation["available_actions"]
valid_actions[valid_actions_inds] = 1
batch_pc_si.append(frame.state)
batch_pc_a.append(a)
batch_pc_R.append(pc_R)
batch_pc_va.append(valid_actions)
batch_pc_si.reverse()
batch_pc_a.reverse()
batch_pc_R.reverse()
batch_pc_va.reverse
pc_feed_dict = {
self.pc_input: batch_pc_si,
self.pc_a: batch_pc_a,
self.pc_r: batch_pc_R,
self.pc_valid_non_spatial_action: batch_pc_va
}
feed.update(pc_feed_dict)
_, summary = self.sess.run([self.train_op, self.summary_op],
feed_dict=feed)
self.summary_writer.add_summary(summary, cter)
def supervised_train(training_episode):
# Initialization
EPISODES, episode, max_average = 20000, 0, 50.0 # specific for pong
while episode < training_episode:
if episode < EPISODES:
episode += 1
replay = trajectory.Trajectory(
'/media/kimbring2/Steam/StarCraftII/Replays/', 'Terran', 'Terran',
2500)
replay.get_random_trajectory()
replay_index = 0
home_replay_done = False
home_replay_feature_screen_list, home_replay_feature_player_list, home_replay_available_actions_list = [], [], []
home_replay_fn_id_list, home_replay_arg_ids_list = [], []
home_replay_memory_state_list, home_replay_carry_state_list = [], []
memory_state = np.zeros([1, 256], dtype=np.float32)
carry_state = np.zeros([1, 256], dtype=np.float32)
while not home_replay_done:
home_replay_state = replay.home_trajectory[replay_index][0]
home_replay_actions = replay.home_trajectory[replay_index][1]
home_replay_done = replay.home_trajectory[replay_index][2]
home_replay_feature_screen = home_replay_state['feature_screen']
home_replay_feature_screen = preprocess_screen(
home_replay_feature_screen)
home_replay_feature_screen = np.transpose(
home_replay_feature_screen, (1, 2, 0))
home_replay_feature_player = home_replay_state['player']
home_replay_feature_player = preprocess_player(
home_replay_feature_player)
home_replay_available_actions = home_replay_state[
'available_actions']
home_replay_available_actions = preprocess_available_actions(
home_replay_available_actions)
home_replay_feature_screen_array = np.array(
[home_replay_feature_screen])
home_replay_feature_player_array = np.array(
[home_replay_feature_player])
home_replay_available_actions_array = np.array(
[home_replay_available_actions])
home_replay_feature_screen_list.append(
home_replay_feature_screen_array)
home_replay_feature_player_list.append(
home_replay_feature_player_array)
home_replay_available_actions_list.append(
home_replay_available_actions_array)
home_replay_memory_state_list.append(memory_state)
home_replay_carry_state_list.append(carry_state)
home_replay_prediction = home_agent.act(
home_replay_feature_screen_array,
home_replay_feature_player_array,
home_replay_available_actions_array, memory_state, carry_state)
home_replay_next_memory_state = home_replay_prediction[3]
home_replay_next_carry_state = home_replay_prediction[4]
home_replay_action = random.choice(home_replay_actions)
home_replay_fn_id = int(home_replay_action.function)
home_replay_args_ids = dict()
for arg_type in actions.TYPES:
home_replay_args_ids[arg_type] = -1
arg_index = 0
for arg_type in FUNCTIONS._func_list[home_replay_fn_id].args:
home_replay_args_ids[arg_type] = home_replay_action.arguments[
arg_index]
arg_index += 1
home_replay_fn_id_list.append(home_replay_fn_id)
home_replay_arg_id_list = []
for arg_type in home_replay_args_ids.keys():
arg_id = home_replay_args_ids[arg_type]
if type(arg_id) == list:
if len(arg_id) == 2:
arg_id = arg_id[0] * feature_screen_size + arg_id[1]
else:
arg_id = int(arg_id[0])
home_replay_arg_id_list.append(arg_id)
home_replay_arg_ids_list.append(np.array([home_replay_arg_id_list
]))
if home_replay_done == StepType.LAST:
home_replay_done = True
else:
home_replay_done = False
if home_replay_done:
break
replay_index += 1
#print("replay_index: ", replay_index)
if replay_index >= len(replay.home_trajectory) - 1:
break
memory_state = home_replay_next_memory_state
carry_state = home_replay_next_carry_state
if len(home_replay_feature_screen_list) == 16:
if arguments.training == True:
home_agent.supervised_replay(
home_replay_feature_screen_list,
home_replay_feature_player_list,
home_replay_available_actions_list,
home_replay_fn_id_list, home_replay_arg_ids_list,
home_replay_memory_state_list,
home_replay_carry_state_list)
home_replay_feature_screen_list, home_replay_feature_player_list, home_replay_available_actions_list = [], [], []
home_replay_fn_id_list, home_replay_arg_ids_list = [], []
home_replay_memory_state_list, home_replay_carry_state_list = [], []
def update(self, rbs, disc, lr, cter):
# Compute R, which is value of the last observation
obs = rbs[-1][-1]
if obs.last():
R = 0
else:
minimap = np.array(obs.observation['feature_minimap'],
dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['feature_screen'],
dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
feed = {
self.minimap: minimap,
self.screen: screen,
self.info: info
}
R = self.sess.run(self.value, feed_dict=feed)[0]
# Compute targets and masks
minimaps = []
screens = []
infos = []
value_target = np.zeros([len(rbs)], dtype=np.float32)
value_target[-1] = R
valid_spatial_action = np.zeros([len(rbs)], dtype=np.float32)
spatial_action_selected = np.zeros([len(rbs), self.ssize**2],
dtype=np.float32)
valid_non_spatial_action = np.zeros(
[len(rbs), len(actions.FUNCTIONS)], dtype=np.float32)
non_spatial_action_selected = np.zeros(
[len(rbs), len(actions.FUNCTIONS)], dtype=np.float32)
rbs.reverse()
for i, [obs, action, next_obs] in enumerate(rbs):
minimap = np.array(obs.observation['feature_minimap'],
dtype=np.float32)
minimap = np.expand_dims(U.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['feature_screen'],
dtype=np.float32)
screen = np.expand_dims(U.preprocess_screen(screen), axis=0)
info = np.zeros([1, self.isize], dtype=np.float32)
info[0, obs.observation['available_actions']] = 1
minimaps.append(minimap)
screens.append(screen)
infos.append(info)
self.reward = obs.reward
act_id = action.function
act_args = action.arguments
value_target[i] = self.reward + disc * value_target[i - 1]
valid_actions = obs.observation["available_actions"]
valid_non_spatial_action[i, valid_actions] = 1
non_spatial_action_selected[i, act_id] = 1
args = actions.FUNCTIONS[act_id].args
for arg, act_arg in zip(args, act_args):
if arg.name in ('screen', 'minimap', 'screen2'):
ind = act_arg[1] * self.ssize + act_arg[0]
valid_spatial_action[i] = 1
spatial_action_selected[i, ind] = 1
minimaps = np.concatenate(minimaps, axis=0)
screens = np.concatenate(screens, axis=0)
infos = np.concatenate(infos, axis=0)
# Train
feed = {
self.minimap: minimaps,
self.screen: screens,
self.info: infos,
self.value_target: value_target,
self.valid_spatial_action: valid_spatial_action,
self.spatial_action_selected: spatial_action_selected,
self.valid_non_spatial_action: valid_non_spatial_action,
self.non_spatial_action_selected: non_spatial_action_selected,
self.learning_rate: lr,
self.score: self.reward
} # will this work?
_, summary = self.sess.run([self.train_op, self.summary_op],
feed_dict=feed)
self.summary_writer.add_summary(summary, cter)
def reinforcement_train(training_episode):
score_list = []
max_average = 5.0
EPISODES, episode, max_average = 20000, 0, 5.0
while episode < training_episode:
# Reset episode
home_score, home_done, SAVING = 0, False, ''
opponent_score, opponent_done = 0, False
state = env.reset()
home_feature_screen_list, home_feature_player_list, home_feature_units_list, home_available_actions_list = [], [], [], []
home_fn_id_list, home_arg_ids_list, home_rewards, home_dones = [], [], [], []
home_memory_state_list, home_carry_state_list = [], []
memory_state = np.zeros([1, 256], dtype=np.float32)
carry_state = np.zeros([1, 256], dtype=np.float32)
while not home_done:
home_state = state[0]
#opponent_state = state[1]
home_feature_screen = home_state[3]['feature_screen']
home_feature_screen = preprocess_screen(home_feature_screen)
home_feature_screen = np.transpose(home_feature_screen, (1, 2, 0))
home_feature_player = home_state[3]['player']
home_feature_player = preprocess_player(home_feature_player)
home_available_actions = home_state[3]['available_actions']
home_available_actions = preprocess_available_actions(
home_available_actions)
home_feature_units = home_state[3]['feature_units']
home_feature_units = preprocess_feature_units(
home_feature_units, feature_screen_size)
#print("home_feature_units.shape: ", home_feature_units.shape)
home_feature_screen_array = np.array([home_feature_screen])
home_feature_player_array = np.array([home_feature_player])
home_feature_units_array = np.array([home_feature_units])
home_available_actions_array = np.array([home_available_actions])
home_feature_screen_list.append(home_feature_screen_array)
home_feature_player_list.append(home_feature_player_array)
home_feature_units_list.append(home_feature_units_array)
home_available_actions_list.append([home_available_actions])
home_memory_state_list.append(memory_state)
home_carry_state_list.append(carry_state)
home_prediction = home_agent.act(home_feature_screen_array,
home_feature_player_array,
home_feature_units_array,
home_available_actions_array,
memory_state, carry_state)
home_fn_pi = home_prediction[0]
home_arg_pis = home_prediction[1]
home_next_memory_state = home_prediction[3]
home_next_carry_state = home_prediction[4]
home_fn_samples, home_arg_samples = sample_actions(
home_available_actions, home_fn_pi, home_arg_pis)
home_fn_id, home_arg_ids = mask_unused_argument_samples(
home_fn_samples, home_arg_samples)
home_fn_id_list.append(home_fn_id[0])
home_arg_id_list = []
for arg_type in home_arg_ids.keys():
arg_id = home_arg_ids[arg_type]
home_arg_id_list.append(arg_id)
home_arg_ids_list.append(np.array([home_arg_id_list]))
home_actions_list = actions_to_pysc2(home_fn_id, home_arg_ids,
(32, 32))
actions_list = [home_actions_list, actions.FUNCTIONS.no_op()]
next_state = env.step(actions_list)
home_next_state = next_state[0]
home_done = home_next_state[0]
if home_done == StepType.LAST:
home_done = True
else:
home_done = False
state = next_state
memory_state = home_next_memory_state
carry_state = home_next_carry_state
home_reward = float(home_next_state[1])
home_rewards.append(home_reward)
home_dones.append(home_done)
home_score += home_reward
state = next_state
if len(home_feature_screen_list) == 16:
if arguments.training == True:
home_agent.reinforcement_replay(
home_feature_screen_list, home_feature_player_list,
home_feature_units_list, home_available_actions_list,
home_fn_id_list, home_arg_ids_list, home_rewards,
home_dones, home_memory_state_list,
home_carry_state_list)
home_feature_screen_list, home_feature_player_list, home_feature_units_list, home_available_actions_list = [], [], [], []
home_fn_id_list, home_arg_ids_list, home_rewards, home_dones = [], [], [], []
home_memory_state_list, home_carry_state_list = [], []
score_list.append(home_score)
average = sum(score_list) / len(score_list)
PlotModel(home_score, episode)
print("episode: {}/{}, score: {}, average: {:.2f} {}".format(
episode, EPISODES, home_score, average, SAVING))
if episode < EPISODES:
episode += 1
env.close()
