def test_static_env():
from cchess_alphazero.environment.env import CChessEnv
import cchess_alphazero.environment.static_env as senv
from cchess_alphazero.environment.static_env import INIT_STATE
from cchess_alphazero.environment.lookup_tables import flip_move
env = CChessEnv()
env.reset()
print("env: " + env.observation)
print("senv: " + INIT_STATE)
state = INIT_STATE
env.step('0001')
state = senv.step(state, '0001')
print(senv.evaluate(state))
print("env: " + env.observation)
print("senv: " + state)
env.step('7770')
state = senv.step(state, flip_move('7770'))
print(senv.evaluate(state))
print("env: " + env.observation)
print("senv: " + state)
env.render()
board = senv.state_to_board(state)
for i in range(9, -1, -1):
print(board[i])
print("env: ")
print(env.input_planes()[0+7:3+7])
print("senv: ")
print(senv.state_to_planes(state)[0+7:3+7])
print(f"env: {env.board.legal_moves()}" )
print(f"senv: {senv.get_legal_moves(state)}")
print(set(env.board.legal_moves()) == set(senv.get_legal_moves(state)))
def expand_and_evaluate(self, state, history):
'''
Evaluate the state, return its policy and value computed by neural network
'''
state_planes = senv.state_to_planes(state)
with self.q_lock:
self.buffer_planes.append(state_planes)
self.buffer_history.append(history)
def expand_and_evaluate(self, state, history):
'''
Evaluate the state, return its policy and value computed by neural network
'''
state_planes = senv.state_to_planes(state)
with self.q_lock:
self.buffer_planes.append(state_planes)
self.buffer_history.append(history)
def convert_to_trainging_data(data):
state_list = []
policy_list = []
value_list = []
for state, policy, value in data:
state_planes = senv.state_to_planes(state)
sl_value = value
state_list.append(state_planes)
policy_list.append(policy)
value_list.append(sl_value)
return np.asarray(state_list, dtype=np.float32), \
np.asarray(policy_list, dtype=np.float32), \
np.asarray(value_list, dtype=np.float32)
def expand_and_evaluate(self, state, history, real_hist=None):
'''
Evaluate the state, return its policy and value computed by neural network
'''
if self.use_history:
if real_hist:
# logger.debug(f"real history = {real_hist}")
state_planes = senv.state_history_to_planes(state, real_hist)
else:
# logger.debug(f"history = {history}")
state_planes = senv.state_history_to_planes(state, history)
else:
state_planes = senv.state_to_planes(state)
with self.q_lock:
self.buffer_planes.append(state_planes)
self.buffer_history.append(history)
def convert_to_trainging_data(data):
state_list = []
policy_list = []
value_list = []
for state, policy, value in data:
state_planes = senv.state_to_planes(state)
sl_value = value
state_list.append(state_planes)
policy_list.append(policy)
value_list.append(sl_value)
return np.asarray(state_list, dtype=np.float32), \
np.asarray(policy_list, dtype=np.float32), \
np.asarray(value_list, dtype=np.float32)
def convert_to_trainging_data(data, history):
state_list = []
policy_list = []
value_list = []
i = 0
for state, policy, value in data:
if history is None:
state_planes = senv.state_to_planes(state)
else:
state_planes = senv.state_history_to_planes(
state, history[0:i * 2 + 1])
sl_value = value
state_list.append(state_planes)
policy_list.append(policy)
value_list.append(sl_value)
i += 1
return np.asarray(state_list, dtype=np.float32), \
np.asarray(policy_list, dtype=np.float32), \
np.asarray(value_list, dtype=np.float32)
