def play_right_add(self, verbose=False):
# note this is a right version of game play, which also add input and action
prev_state = None
prev_action = None
prev_value = None
prev_add_state = None
prev_map_state = None
show_image = False
self.safe_action(C._NO_OP, 0, [])
self.safe_action(C._MOVE_CAMERA, 0, [C.base_camera_pos])
self._gases = U.find_initial_gases(self.obs)
self.dummy_add_state = np.zeros(1)
self.dummy_map_state = np.zeros([1, 1, 1])
simulate_seconds = 0
feature_dict = U.edge_state()
previous_match = -1
while True:
self.safe_action(C._MOVE_CAMERA, 0, [C.base_camera_pos])
if self.policy_flag and (not self.is_end):
# get the state
state = self.mapping_source_to_mini_by_rule(
self.get_the_input_right(self.obs))
if self.image_debug and verbose and self.step % C.time_wait(
self.image_wait_secs) == 0:
show_image = True
else:
show_image = False
if verbose:
print('show_image:', show_image)
map_state = U.get_small_simple_map_data(
self.obs, show_image, show_image)
if verbose:
print('map_state.shape:', map_state.shape)
add_state = self.get_add_state(
self.get_the_input_right(self.obs))
# get the action and value accoding to state
#print("add_state:", add_state)
if self.ob_space_add == 0:
add_state = self.dummy_add_state
map_state = self.dummy_map_state = np.zeros([1, 1, 1])
action, action_probs, value = self.net.policy.get_act_action_probs(
state, add_state, map_state, verbose=verbose)
# if this is not the fisrt state, store things to buffer
if prev_state is not None:
# try reward = self.obs.reward
reward = self.obs.reward
if verbose:
print(prev_state, prev_add_state, prev_action, state,
reward, prev_value, value)
self.local_buffer.append_more_more(
prev_state, prev_add_state, prev_map_state,
prev_action, state, reward, prev_value, value)
self.mini_step(action)
simulate_seconds += self.policy_wait_secs
# the evn step to new states
prev_state = state
prev_action = action
prev_value = value
prev_add_state = add_state
prev_map_state = map_state
self.policy_flag = False
if self.is_end:
# get the last state and reward
# get the state
state = self.mapping_source_to_mini_by_rule(
self.get_the_input_right(self.obs))
map_state = U.get_small_simple_map_data(self.obs)
add_state = self.get_add_state(
self.get_the_input_right(self.obs))
if self.ob_space_add == 0:
add_state = self.dummy_add_state
map_state = self.dummy_map_state = np.zeros([1, 1, 1])
value = self.net.policy.get_values(state, add_state, map_state)
# the value of the last state is defined somewhat different
value = self.get_values_right(value)
# if this is not the fisrt state, store things to buffer
if prev_state is not None:
reward = self.obs.reward
if verbose:
print(prev_state, prev_add_state, prev_action, state,
reward, prev_value, value)
self.local_buffer.append_more_more(
prev_state, prev_add_state, prev_map_state,
prev_action, state, reward, prev_value, value)
break
if self.rl_training:
#print(self.local_buffer.values)
#print(self.local_buffer.values_next)
#print(self.local_buffer.rewards)
self.global_buffer.add(self.local_buffer)
print("add map bn:")
print("add %d buffer!" % (len(self.local_buffer.rewards)))
def set_flag(self):
if self.step % C.time_wait(self.strategy_wait_secs) == 1:
self.strategy_flag = True
if self.step % C.time_wait(self.policy_wait_secs) == 1:
self.policy_flag = True
def play_right_add(self, verbose=False):
# note this is a right version of game play, which also add input and action
prev_state = None
prev_action = None
prev_value = None
prev_add_state = None
prev_map_state = None
show_image = False
self.safe_action(C._NO_OP, 0, [])
self.safe_action(C._MOVE_CAMERA, 0, [C.base_camera_pos])
self._gases = U.find_initial_gases(self.obs)
self.dummy_add_state = np.zeros(1)
self.dummy_map_state = np.zeros([1, 1, 1])
simulate_seconds = 0
feature_dict = U.edge_state()
previous_match = -1
while True:
self.safe_action(C._MOVE_CAMERA, 0, [C.base_camera_pos])
if self.policy_flag and (not self.is_end):
# get the state
state = self.mapping_source_to_mini_by_rule(
self.get_the_input_right(self.obs))
if self.image_debug and verbose and self.step % C.time_wait(
self.image_wait_secs) == 0:
show_image = True
else:
show_image = False
if verbose:
print('show_image:', show_image)
map_state = U.get_small_simple_map_data(
self.obs, show_image, show_image)
if verbose:
print('map_state.shape:', map_state.shape)
add_state = self.get_add_state(
self.get_the_input_right(self.obs))
# get the action and value accoding to state
#print("add_state:", add_state)
if self.ob_space_add == 0:
add_state = self.dummy_add_state
map_state = self.dummy_map_state = np.zeros([1, 1, 1])
action, action_probs, value = self.net.policy.get_act_action_probs(
state, add_state, map_state, verbose=verbose)
if self.probe_debug and verbose and self.step % C.time_wait(
self.prove_save_wait_secs) == 0:
save_prob = True
else:
save_prob = False
if self.action_prob_debug and verbose:
print('self.step:', self.step)
print(self.prob_show_wait_seconds)
print('simulate_seconds:', simulate_seconds)
use_TG = True
if use_TG:
bar_type = 'TG'
max_y = 0.3
color = 'b'
else:
bar_type = 'RG'
max_y = 0.5
color = 'r'
bar_name = bar_type + '_' + str(simulate_seconds)
if True and simulate_seconds in self.prob_show_wait_seconds:
pprint.pprint(action_probs)
U.show_prob_dist(action_probs,
show=True,
color=color,
max_y=max_y,
action_num=self.action_num,
save=False,
name=bar_name,
count=0)
if self.edge_state_debug and verbose:
match_list = U.calculate_state_mapping(state, feature_dict)
print('state:', state)
print('match_list:', match_list)
for i, match in enumerate(match_list):
if match and i != previous_match:
print('Match:', i + 1)
match_name = 'ES' + '_' + str(i + 1) + '_' + str(
simulate_seconds)
U.show_prob_dist(action_probs,
show=True,
color=color,
max_y=max_y,
action_num=self.action_num,
save=True,
name=match_name,
count=0)
previous_match = i
# if this is not the fisrt state, store things to buffer
if prev_state is not None:
# try reward = self.obs.reward
reward = self.obs.reward
if verbose:
print(prev_state, prev_add_state, prev_action, state,
reward, prev_value, value)
self.local_buffer.append_more_more(
prev_state, prev_add_state, prev_map_state,
prev_action, state, reward, prev_value, value)
self.mini_step(action)
simulate_seconds += self.policy_wait_secs
# the evn step to new states
prev_state = state
prev_action = action
prev_value = value
prev_add_state = add_state
prev_map_state = map_state
self.policy_flag = False
if self.is_end:
# get the last state and reward
# get the state
state = self.mapping_source_to_mini_by_rule(
self.get_the_input_right(self.obs))
map_state = U.get_small_simple_map_data(self.obs)
add_state = self.get_add_state(
self.get_the_input_right(self.obs))
if self.ob_space_add == 0:
add_state = self.dummy_add_state
map_state = self.dummy_map_state = np.zeros([1, 1, 1])
value = self.net.policy.get_values(state, add_state, map_state)
# the value of the last state is defined somewhat different
value = self.get_values_right(value)
# if this is not the fisrt state, store things to buffer
if prev_state is not None:
reward = self.obs.reward
if verbose:
print(prev_state, prev_add_state, prev_action, state,
reward, prev_value, value)
self.local_buffer.append_more_more(
prev_state, prev_add_state, prev_map_state,
prev_action, state, reward, prev_value, value)
break
if self.rl_training:
#print(self.local_buffer.values)
#print(self.local_buffer.values_next)
#print(self.local_buffer.rewards)
self.global_buffer.add(self.local_buffer)
print("add map bn:")
print("add %d buffer!" % (len(self.local_buffer.rewards)))
