def observation(self, observation: State) -> np.ndarray:
"""
:param observation: as defined in ContinuousSimulation
:return: np.ndarray with info in this order
station_locations, station_occs, station_maxes, car_locs
car_dest_idx, car_dest_loc, t, query_loc
dtype: float32
size: (n_stations * 4) + (max_cars * 5) + 3
"""
max_cars: int = self.env.max_cars
observation: Dict = observation._asdict()
arrs: List[np.ndarray] = []
for key in observation.keys():
if key == 'station_idx':
pass
# station indices are constant - ignore them
correct_shape: Tuple[int] = self.env.observation_space[key].shape
# the shape specified in observation_space isn't accurate, because
# the dimensions are variable. Here, we're fixing it to max size
padded: np.ndarray = self._pad_zeros_(observation[key], correct_shape)
# np.ndarray[Any] : correct_shape
flattened: np.ndarray = padded.flatten().astype(np.float32)
arrs.append(flattened)
return np.concatenate(arrs, axis=0)
def len_to_finish(self, state):
# 如果在区域内，计算点到目标到距离
if self.area.state_in(state):
return State.getDist(state, self.goal)
# 否则计算点到区域中心+区域中心到goal的距离
else:
return State.getDist(state, self.area.center) \
+ State.getDist(self.area.center, self.goal)
def get_random_state():
state = State()
symbol = np.random.choice([-1, 1])
for i in range(BOARD_ROWS):
for j in range(BOARD_COLS):
escolha = np.random.choice([0, symbol])
state = state.next_state(i, j, escolha)
if escolha == symbol:
symbol = -symbol
return state
def state_from_ctype(ctype_state):
s = State()
s.pos = ctype_state.pos
s.state = int(ctype_state.state.decode(), 16)
s.history = ctype_state.history
s.move = ctype_state.move
s.no_change_count = ctype_state.no_change_count
s.depth = ctype_state.depth
return s
def __init__(self):
# 环境初始化
self.global_arg = arg.init_global_arg()
env_arg = arg.init_env_arg(self.global_arg)
# 增加nk的一个读入操作
self.main_env = Env(env_arg)
for model_type in ['st', 'ed']:
if all_config['checkpoint']['env'][model_type]['enable']:
self.main_env.nkmodel_load(all_config['checkpoint']['env']['path'], model_type)
self.main_env.nkmodel_save(all_config["nkmodel_path"][model_type], model_type)
# 个体初始化
self.agents = []
csv_head_agent = ['agent_no'] + ['st_state'] + ['st_value'] + ['insight'] + ['xplr'] + ['xplt'] + ['enable']
moniter.AppendToCsv(csv_head_agent, all_config['agent_csv_path'])
for i in range(self.global_arg["Nagent"]):
# 个体随机初始位置
start_st_label = [randint(0, self.main_env.P - 1) for j in range(self.main_env.N)]
state_start = State(start_st_label)
self.agents.append(Agent(arg.init_agent_arg(self.global_arg,
self.main_env.arg),
self.main_env))
self.agents[i].state_now = deepcopy(state_start)
self.agents[i].agent_id = i
# 去除了一开始给一个全局area，改为添加一个包含起点的点area
start_area = Area(self.agents[i].state_now, [False] * self.main_env.N, 0)
start_area.info = get_area_sample_distr(env=self.main_env, area=start_area, state=self.agents[i].state_now,
T_stmp=0, sample_num=1, dfs_r=1)
start_area.sign = Sign(i, 0, 'start')
self.agents[i].renew_m_info(start_area, 0)
self.a_plan = None
logging.info("state:%s, st_value:%s,insight:%.5s ,xplr:%.5s, xplt:%.5s, enable:%.5s" % (
str(self.agents[i].state_now),
self.main_env.getValue(self.agents[i].state_now, 0),
self.agents[i].agent_arg['a']['insight'],
self.agents[i].agent_arg['a']['xplr'],
self.agents[i].agent_arg['a']['xplt'],
self.agents[i].agent_arg['a']['enable']))
# 记录agent信息
csv_info_agent = ['agent%d' % i] \
+ [self.agents[i].state_now] \
+ [self.main_env.getValue(self.agents[i].state_now, 0)] \
+ [self.agents[i].agent_arg['a']['insight']] \
+ [self.agents[i].agent_arg['a']['xplr']] \
+ [self.agents[i].agent_arg['a']['xplt']] \
+ [self.agents[i].agent_arg['a']['enable']]
moniter.AppendToCsv(csv_info_agent, all_config['agent_csv_path'])
# 社会网络初始化
soclnet_arg = arg.init_soclnet_arg(self.global_arg, env_arg)
self.socl_net = SoclNet(soclnet_arg)
self.socl_net.new_flat_init() # 修改初始化方法
# self.socl_net.flat_init()
if all_config['checkpoint']['socl_network']['enable']:
self.socl_net.power_load(all_config['checkpoint']['socl_network']['power'])
self.socl_net.relat_load(all_config['checkpoint']['socl_network']['relat'])
self.record = Record()
self.metric = metrics.register_all_metrics(metrics.Metrics())
def observation(self, observation: State):
return State(station_idx=observation.station_idx,
station_locations=observation.station_locations,
station_occs=observation.station_occs,
station_maxes=observation.station_maxes,
car_locs=observation.car_locs,
car_dest_idx=observation.car_dest_idx,
car_dest_loc=observation.car_dest_loc,
t=self.encoder.encode(observation.t),
query_loc=observation.query_loc,
remaining_queries=observation.remaining_queries)
def astar_h2_solution(self, ):
print(
'[===============================A* Heuristic2 Solution===============================]'
)
sTime = time.time()
game = State(bfs_board, dfs_goal, 7, 7, count_pegs(dfs_board))
print(game)
solution = self.aStar(game, self.heuristic2)
eTime = time.time()
self.print_solution(solution)
print('Total Time: approx. %s sec \n' % str(eTime - sTime))
def bfs_solution(self, ):
print(
'[===============================BFS Solution===============================]'
)
sTime = time.time()
game = State(bfs_board, bfs_goal, 7, 7, count_pegs(bfs_board))
print(game)
solution = self.bfs(game)
eTime = time.time()
self.print_solution(solution)
print('Total Time: approx. %s sec \n' % str(eTime - sTime))
def observation(self, observation: State) -> State:
new_t: float = (observation.t / self._max_t).astype(np.float32)
return State(station_idx=observation.station_idx,
station_locations=observation.station_locations,
station_occs=observation.station_occs,
station_maxes=observation.station_maxes,
car_locs=observation.car_locs,
car_dest_idx=observation.car_dest_idx,
car_dest_loc=observation.car_dest_loc,
t=new_t,
query_loc=observation.query_loc,
remaining_queries=observation.remaining_queries)
def observation(self, observation: State):
return State(station_idx=observation.station_idx,
station_locations=self._encode_loc(
observation.station_locations),
station_occs=observation.station_occs,
station_maxes=observation.station_maxes,
car_locs=self._encode_loc(observation.car_locs),
car_dest_idx=observation.car_dest_idx,
car_dest_loc=self._encode_loc(observation.car_dest_loc),
t=observation.t,
query_loc=self._encode_loc(observation.query_loc),
remaining_queries=observation.remaining_queries)
def __init__(self, step_size=0.1, epsilon=0.1, symbol=0):
self.step_size = step_size
self.epsilon = epsilon
self.previous_state = State()
self.state = None
self.symbol = symbol
self.td_errors = []
self.estimator = Estimator()
self.policy = make_epsilon_greedy_policy(self.estimator)
self.action = (0, 0)
self.actions = []
for i in range(BOARD_ROWS):
for j in range(BOARD_COLS):
self.actions.append((i, j))
def observation(self,
observation: State) -> State:
car_indices: np.ndarray = np_onehot(observation.car_dest_idx, max=self.n_stations-1)
# np.ndarray[int32] : [cur_n_cars, 1, self.n_stations]
car_indices= car_indices[:, 0, :]
# [cur_n_cars, self.n_stations]
station_indices: np.ndarray = np_onehot(observation.station_idx, max=self.n_stations-1)
station_indices = station_indices[:, 0, :]
# [n_stations, n_stations] should be eye(n_stations)
return State(
station_idx=station_indices,
station_locations=observation.station_locations,
station_maxes=observation.station_maxes,
station_occs=observation.station_occs,
car_dest_loc=observation.car_dest_loc,
car_dest_idx=car_indices,
car_locs=observation.car_locs,
query_loc=observation.query_loc,
t=observation.t,
remaining_queries=observation.remaining_queries)
def __init__(self):
observations = [get_random_state().data.reshape(-1) for _ in range(10000)]
self.scaler = sklearn.preprocessing.StandardScaler()
self.scaler.fit(observations)
self.featurizer = sklearn.pipeline.FeatureUnion([
("rbf1", RBFSampler(gamma=5.0, n_components=150)),
("rbf2", RBFSampler(gamma=2.0, n_components=150)),
("rbf3", RBFSampler(gamma=1.0, n_components=150)),
("rbf4", RBFSampler(gamma=0.5, n_components=150))
], n_jobs=1)
# self.featurizer.fit(observations)
self.featurizer.fit(self.scaler.transform(observations))
self.models = dict()
for i in range(BOARD_ROWS):
for j in range(BOARD_COLS):
model = SGDRegressor(learning_rate="constant")
model.partial_fit([self.featurize_state(State())], [0])
self.models[(i,j)] = model
def run_exp(self):
up_info = {}
for i in range(self.global_arg["Nagent"]):
self.agents.append(
Agent(arg.init_agent_arg(self.global_arg, self.main_env.arg)))
self.agents[i].state_now = State(
[0 for _ in range(self.main_env.N)])
self.agents[i].inter_area.info = get_area_sample_distr(
env=self.main_env,
T=0,
area=self.agents[i].inter_area,
state=self.agents[i].state_now,
sample_num=self.main_env.arg['ACT']['hqxx']['sample_n'],
dfs_r=self.main_env.arg['ACT']['hqxx']['dfs_p'])
self.agents[i].inter_area.info['start_t'] = 0
stage_num = self.global_arg['T'] // self.global_arg['Ts']
for k in range(self.global_arg["Nagent"]):
csv_head = [
'frame', 'SSMfi', 'SSM_f-req', 'proc_action', 'SSM_f_need',
'nkmax', 'nkmin', 'nkmid', 'nkavg', 'nk0.75', "nk0.25"
]
# 'peakmax', 'peakmin', 'peakmid', 'peakavg', 'peak0.75', "peak0.25"]
moniter.AppendToCsv(csv_head, all_config['result_csv_path'][k])
csv_head = ['frame'] \
+ ["%s%d" % (using_brain[k].func_name, k) for k in range(self.global_arg['Nagent'])] \
+ ["agent_avg"] \
+ ['nkmax', 'nkmin', 'nkmid', 'nkavg', 'nk0.75', "nk0.25"]
moniter.AppendToCsv(csv_head, all_config['result_csv_path'][-1])
for i in range(stage_num):
Ti = i * self.global_arg['Ts'] + 1
logging.info("stage %3d , Ti:%3d" % (i, Ti))
up_info['nkinfo'] = self.main_env.getModelDistri(Ti)
# up_info['nk_peak'] = self.main_env.getModelPeakDistri(Ti)
# 运行一个Stage，Ti表示每个Stage的第一帧
self.run_stage(Ti, up_info)
def play(self, train=False, print_state=False):
alternator = self.alternate()
current_state = State()
self.p1.set_state(current_state)
self.p2.set_state(current_state)
if print_state:
current_state.print_state()
while True:
player = next(alternator)
current_state, is_end = player.act()
if print_state:
current_state.print_state()
if is_end:
if train:
player = next(alternator)
player.backup(current_state, True)
return current_state.winner
self.p1.set_state(current_state)
self.p2.set_state(current_state)
def bfs_multiproccess(file, env, no_change_cnt, truncate):
print_time = time.time()
if math.ceil(env.state_len / 8) >= STATE_LEN:
print(f"Increase state len to {math.ceil(env.state_len/4)}")
return None
queues = []
for i in range(num_proc):
queues.append(Manager().Queue())
hashes = dict()
lis = []
lock = Lock()
init_state = env.get_init_state()
init_state.history = b''
init_state.move = b'N'
lis.append(ctype_state(init_state)) # push the initial state
found_state = None
count = 0
max_shared_block = 100000
best_depth = 200
while len(lis) > 0:
act_proc = min(max(1, len(lis)), num_proc)
all_list = lis
lis = []
chunk_count = 0
for pos_in_all_lis in range(0, len(all_list), max_shared_block):
chunk_size = math.ceil(
min(max_shared_block, len(all_list)) / num_proc)
chunk_count += 1
#print_lock(lock, f"Makeing shared memory at {pos_in_all_lis}-{pos_in_all_lis+max_shared_block} ({chunk_count}/{math.ceil(len(all_list)/max_shared_block)})")
# print_tuple_state("copy to shared mem:",x,lock)
shared_array = Array(CState,
all_list[pos_in_all_lis:pos_in_all_lis +
max_shared_block],
lock=False)
#print_lock(lock, f"making input for processes. all_size:{len(all_list)} chunk_size:{chunk_size}")
input = [(i, queues[i], env, shared_array, i * chunk_size,
chunk_size, lock, no_change_cnt)
for i in list(range(act_proc))]
#print_lock(lock, f"Starting {len(input)} jobs")
pool = []
for i in input:
proc = Process(target=bfs_job, args=(i, ))
pool.append(proc)
for p in pool:
p.start()
while any(pool):
proc_loc = -1
while True:
for p in range(len(pool)):
if pool[p] and not pool[p].is_alive():
proc_loc = p
if proc_loc != -1:
break
time.sleep(0.01)
pool[proc_loc].join()
#print_lock(lock, f'Pulling from process {proc_loc}')
for state in queues[proc_loc].get():
if state[5] > best_depth:
continue
s = int(state[1].decode(), 16)
state_hash = (state[0] << env.state_len) + s
#if state_hash in hashes and hashes[state_hash] <= state[5]:
if hashes.get(state_hash, 200) <= state[5]:
continue
hashes[state_hash] = state[5]
lis.append(state)
if s == env.goal:
found_state = State()
found_state.history = state[2]
best_depth = state[5]
#print_lock(lock, f'{s} {found_state.history}')
#return DummyState(list(found_state.history))
#print_lock(lock, f'Completed pulling from process {proc_loc}')
pool[proc_loc].terminate()
pool[proc_loc] = None
count += 1
#print(f'truncating hash {len(hashes)}')
if truncate:
truncate_hash(hashes, count)
t = "{0:.3f}".format(time.time() - print_time)
print(
f'\r{t} :=======> count:{count} tree:{len(lis)} hash:{len(hashes)} found:{"true" if found_state else "false"}',
end='')
#print(f'{t} :=======> count:{count} tree:{len(lis)} hash:{len(hashes)}')
print("\r", end='')
if not found_state:
print(f'"{file} solution not found !!!')
return None
else:
print(f'{file} found solution at level {best_depth}')
return DummyState([chr(x).encode() for x in found_state.history])
def grow(self) -> bool:
actions, log_probs = self.model(self.state, topk=self.beam_size)
x = log_probs.view(
-1, self.beam_size,
self.beam_size) # batch_size x beam_size x beam_size
x = self.log_probs[~self.finished].unsqueeze(-1) + x
y = x.view(-1, self.beam_size *
self.beam_size) # batch_size x (beam_size * beam_size)
values, indices = y.topk(self.beam_size, dim=-1)
self.log_probs[~self.finished] = values
partial_trees = []
tokens_word = []
tokens_emb = []
batch_idx = []
cnt = 0
for i in range(self.batch_size):
if self.finished[i]:
continue
for j in range(self.beam_size):
idx = indices[cnt, j].item()
m = cnt * self.beam_size + idx // self.beam_size
n = idx % self.beam_size
current_tree = self.state.partial_trees[m]
action = actions[m][n]
tag = self.tags[i][self.n_step]
word = self.tokens_word[i][self.n_step]
tree = AttachJuxtapose.execute(
current_tree,
action,
self.n_step,
tag,
word,
immutable=True,
)
assert isinstance(tree, InternalParseNode)
if self.n_step >= len(self.tokens_word[i]) - 1:
self.finished[i] = True
self.pred_trees[i][j] = tree
else:
partial_trees.append(tree)
tokens_word.append(self.tokens_word[i])
tokens_emb.append(self.tokens_emb[i])
batch_idx.append(self.state.batch_idx[m])
cnt += 1
if tokens_emb == []:
assert self.done()
return True
tokens_emb_t = torch.stack(tokens_emb)
self.n_step += 1
self.state = State(
partial_trees, # type: ignore
tokens_word,
tokens_emb_t,
self.state.next_token_pos.new_full((len(partial_trees), ),
fill_value=self.n_step),
n_step=self.n_step,
batch_idx=batch_idx,
)
return False
def __init__(
self,
tokens_word: List[List[str]],
tags: List[List[str]],
tokens_emb: torch.Tensor,
model: Parser,
cfg: DictConfig,
) -> None:
self.batch_size = len(tokens_word)
self.beam_size = cfg.beam_size
self.tokens_word = tokens_word
self.tokens_emb = tokens_emb
self.tags = tags
self.model = model
device = tokens_emb.device
init_state = State(
[None for _ in range(self.batch_size)],
tokens_word,
tokens_emb,
next_token_pos=torch.zeros(self.batch_size,
dtype=torch.int64,
device=device),
n_step=0,
batch_idx=list(range(self.batch_size)),
)
actions, log_probs = self.model(init_state, topk=self.beam_size)
self.log_probs = log_probs
partial_trees = []
tokens_word_expanded = []
tokens_emb_expanded = []
batch_idx = []
self.finished = torch.zeros(self.batch_size,
dtype=torch.bool,
device=device)
self.pred_trees = [[None for _ in range(self.beam_size)]
for _ in range(self.batch_size)]
for i in range(self.batch_size):
for j in range(self.beam_size):
tag = self.tags[i][0]
word = self.tokens_word[i][0]
tree = AttachJuxtapose.execute(None,
actions[i][j],
0,
tag,
word,
immutable=False)
assert isinstance(tree, InternalParseNode)
if len(self.tokens_word[i]) > 1:
partial_trees.append(tree)
tokens_word_expanded.append(tokens_word[i])
tokens_emb_expanded.append(tokens_emb[i])
batch_idx.append(i)
else:
self.finished[i] = True
self.pred_trees[i][j] = tree
tokens_emb_expanded_t = torch.stack(tokens_emb_expanded)
self.state = State(
partial_trees, # type: ignore
tokens_word_expanded,
tokens_emb_expanded_t,
next_token_pos=torch.ones(len(partial_trees),
dtype=torch.int64,
device=device),
n_step=1,
batch_idx=batch_idx,
)
self.n_step = 1
