def update(self, rbs):
## backprop after episode
## if not episode end, use newwork to estimate value of last state, else 0
obs = rbs[-1][-1]
if obs.last():
R = 0
else:
screen = np.array(obs.observation['feature_screen'],
dtype=np.float32)
screen = np.expand_dims(PP.preprocess_screen(screen), axis=0)
feed = {self.screen: screen}
R = self.sess.run(self.value, feed_dict=feed)[0]
## prepare input & actions & Q value target
screens = []
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)
rbs.reverse()
for i, [obs, action, _] in enumerate(rbs):
screen = np.array(obs.observation['feature_screen'],
dtype=np.float32)
screen = np.expand_dims(PP.preprocess_screen(screen), axis=0)
screens.append(screen)
reward = obs.reward
act_id = action.function
act_args = action.arguments
value_target[i] = reward + self.discount * value_target[i - 1]
args = actions.FUNCTIONS[act_id].args
for arg, act_arg in zip(args, act_args):
if arg.name in ('screen'):
idx = act_arg[1] * self.ssize + act_arg[0]
valid_spatial_action[i] = 1
spatial_action_selected[i, idx] = 1
screens = np.concatenate(screens, axis=0)
## backprop
feed = {
self.screen: screens,
self.value_target: value_target,
self.valid_spatial_action: valid_spatial_action,
self.spatial_action_selected: spatial_action_selected,
self.learning_rate: self.lr
}
_, summary = self.sess.run([self.train_op, self.summary_op],
feed_dict=feed)
self.summary_writer.add_summary(summary, self.cur_episode)
def step(self, obs):
## feed to network
minimap = np.array(obs.observation['feature_minimap'],
dtype=np.float32)
minimap = np.expand_dims(PP.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['feature_screen'], dtype=np.float32)
screen = np.expand_dims(PP.preprocess_screen(screen), axis=0)
structure = np.expand_dims(PP.preprocess_structure(obs), axis=0)
feed = {
self.minimap: minimap,
self.screen: screen,
self.structure: structure
}
non_spatial_action, spatial_action = self.sess.run(
[self.non_spatial_action, self.spatial_action], feed_dict=feed)
## choose spatial and non-spatial action
non_spatial_action = non_spatial_action.ravel()
valid_actions = obs.observation['available_actions']
temp = []
for i in [0, 7, 12]:
if i in valid_actions:
temp.append(i)
valid_actions = temp
for i in [0, 7, 12]:
if i in valid_actions:
temp.append(i)
valid_actions = temp
act_id = valid_actions[np.argmax(non_spatial_action[valid_actions])]
spatial_action = spatial_action.ravel()
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]:
range = int(self.random_range)
dy = np.random.randint(-range, range)
target[0] = int(max(0, min(self.ssize - 1, target[0] + dy)))
dx = np.random.randint(-range, range)
target[1] = int(max(0, min(self.ssize - 1, target[1] + dx)))
## return function
act_args = []
for arg in actions.FUNCTIONS[act_id].args:
if arg.name in ('screen', 'minimap', 'screen2'): ## spatial arg
act_args.append([target[1], target[0]])
else:
act_args.append([0]) ## non-spatial arg
return actions.FunctionCall(act_id, act_args)
def step(self, obs):
## feed to network
screen = np.array(obs.observation['feature_screen'], dtype=np.float32)
screen = np.expand_dims(PP.preprocess_screen(screen), axis=0)
feed = {self.screen: screen}
spatial_action = self.sess.run([self.spatial_action],
feed_dict=feed)[0]
## choose spatial action
spatial_action = spatial_action.ravel()
target = np.argmax(spatial_action)
target = [int(target // self.ssize), int(target % self.ssize)]
# print(target, end=' ')
# print(obs.observation['feature_screen'][5, target[1], target[0]] == 3, end=' ')
## epsilon greedy exploration
if self.training and np.random.rand() < self.epsilon[1]:
range = int(self.random_range)
dy = np.random.randint(-range, range)
target[0] = int(max(0, min(self.ssize - 1, target[0] + dy)))
dx = np.random.randint(-range, range)
target[1] = int(max(0, min(self.ssize - 1, target[1] + dx)))
if 490 in obs.observation.available_actions:
# print('train scv')
return actions.FunctionCall(490, [[0]])
if obs.observation['player'][3] < obs.observation['player'][
4] and obs.observation['player'][1] > 50:
return actions.FunctionCall(2, [[0], [25, 25]])
if not self.walked and len(
obs.observation['multi_select']
) > 0 and 264 in obs.observation.available_actions:
# print('harvest')
# print(target)
self.walked = True
return actions.FunctionCall(264, [[0], target])
if 0 in obs.observation.available_actions and obs.observation[
'player'][-4] == 0:
# print('noop')
return actions.FunctionCall(0, [])
if 6 in obs.observation.available_actions and obs.observation[
'player'][-4] > 0:
# print('select')
self.walked = False
return actions.FunctionCall(6, [[1]])
# print('noop')
return actions.FunctionCall(0, [])
def update(self, rbs):
## backprop after episode
## if not episode end, use newwork to estimate value of last state, else 0
obs = rbs[-1][-1]
if obs.last():
R = 0
else:
minimap = np.array(obs.observation['feature_minimap'],
dtype=np.float32)
minimap = np.expand_dims(PP.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['feature_screen'],
dtype=np.float32)
screen = np.expand_dims(PP.preprocess_screen(screen), axis=0)
structure = np.expand_dims(PP.preprocess_structure(obs), axis=0)
feed = {
self.minimap: minimap,
self.screen: screen,
self.structure: structure
}
R = self.sess.run(self.value, feed_dict=feed)[0]
## prepare input & actions & Q value target
minimaps = []
screens = []
structures = []
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.action_size],
dtype=np.float32)
non_spatial_action_selected = np.zeros([len(rbs), self.action_size],
dtype=np.float32)
rbs.reverse()
for i, [obs, action, _] in enumerate(rbs):
minimap = np.array(obs.observation['feature_minimap'],
dtype=np.float32)
minimap = np.expand_dims(PP.preprocess_minimap(minimap), axis=0)
screen = np.array(obs.observation['feature_screen'],
dtype=np.float32)
screen = np.expand_dims(PP.preprocess_screen(screen), axis=0)
structure = np.expand_dims(PP.preprocess_structure(obs), axis=0)
minimaps.append(minimap)
screens.append(screen)
structures.append(structure)
reward = obs.reward * (obs.observation["score_cumulative"][0] +
1) * 10 # 10 times reward
act_id = action.function
act_args = action.arguments
value_target[i] = reward + self.discount * 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'):
idx = act_arg[1] * self.ssize + act_arg[0]
valid_spatial_action[i] = 1
spatial_action_selected[i, idx] = 1
minimaps = np.concatenate(minimaps, axis=0)
screens = np.concatenate(screens, axis=0)
structures = np.concatenate(structures, axis=0)
## backprop
feed = {
self.minimap: minimaps,
self.screen: screens,
self.structure: structures,
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: self.lr
}
_, summary = self.sess.run([self.train_op, self.summary_op],
feed_dict=feed)
self.summary_writer.add_summary(summary, self.cur_episode)