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 MCTS_search(self, state, history=[], is_root_node=False) -> float:
"""
Monte Carlo Tree Search
"""
while True:
# logger.debug(f"start MCTS, state = {state}, history = {history}")
game_over, v, _ = senv.done(state)
if game_over:
self.executor.submit(self.update_tree, None, v, history)
break
with self.node_lock[state]:
if state not in self.tree:
# Expand and Evaluate
self.tree[state].sum_n = 1
self.tree[state].legal_moves = senv.get_legal_moves(state)
self.tree[state].waiting = True
# logger.debug(f"expand_and_evaluate {state}, sum_n = {self.tree[state].sum_n}, history = {history}")
self.expand_and_evaluate(state, history)
break
if state in history[:-1]: # loop -> loss
# logger.debug(f"loop -> loss, state = {state}, history = {history[:-1]}")
self.executor.submit(self.update_tree, None, 0, history)
break
# Select
node = self.tree[state]
if node.waiting:
node.visit.append(history)
# logger.debug(f"wait for prediction state = {state}")
break
sel_action = self.select_action_q_and_u(state, is_root_node)
virtual_loss = self.config.play.virtual_loss
self.tree[state].sum_n += 1
# logger.debug(f"node = {state}, sum_n = {node.sum_n}")
action_state = self.tree[state].a[sel_action]
action_state.n += virtual_loss
action_state.w -= virtual_loss
action_state.q = action_state.w / action_state.n
# logger.debug(f"apply virtual_loss = {virtual_loss}, as.n = {action_state.n}, w = {action_state.w}, q = {action_state.q}")
if action_state.next is None:
action_state.next = senv.step(state, sel_action)
# logger.debug(f"step action {sel_action}, next = {action_state.next}")
history.append(sel_action)
state = action_state.next
history.append(state)
def MCTS_search(self, state, history=[], is_root_node=False) -> float:
"""
Monte Carlo Tree Search
"""
while True:
# logger.debug(f"start MCTS, state = {state}, history = {history}")
game_over, v, _ = senv.done(state)
if game_over:
self.executor.submit(self.update_tree, None, v, history)
break
with self.node_lock[state]:
if state not in self.tree:
# Expand and Evaluate
self.tree[state].sum_n = 1
self.tree[state].legal_moves = senv.get_legal_moves(state)
self.tree[state].waiting = True
# logger.debug(f"expand_and_evaluate {state}, sum_n = {self.tree[state].sum_n}, history = {history}")
self.expand_and_evaluate(state, history)
break
if state in history[:-1]: # loop -> loss
# logger.debug(f"loop -> loss, state = {state}, history = {history[:-1]}")
self.executor.submit(self.update_tree, None, 0, history)
break
# Select
node = self.tree[state]
if node.waiting:
node.visit.append(history)
# logger.debug(f"wait for prediction state = {state}")
break
sel_action = self.select_action_q_and_u(state, is_root_node)
virtual_loss = self.config.play.virtual_loss
self.tree[state].sum_n += 1
# logger.debug(f"node = {state}, sum_n = {node.sum_n}")
action_state = self.tree[state].a[sel_action]
action_state.n += virtual_loss
action_state.w -= virtual_loss
action_state.q = action_state.w / action_state.n
# logger.debug(f"apply virtual_loss = {virtual_loss}, as.n = {action_state.n}, w = {action_state.w}, q = {action_state.q}")
if action_state.next is None:
action_state.next = senv.step(state, sel_action)
# logger.debug(f"step action {sel_action}, next = {action_state.next}")
history.append(sel_action)
state = action_state.next
history.append(state)
def MCTS_search(self,
state,
history=[],
is_root_node=False,
real_hist=None) -> float:
"""
Monte Carlo Tree Search
"""
while True:
# logger.debug(f"start MCTS, state = {state}, history = {history}")
game_over, v, _ = senv.done(state)
if game_over:
v = v * 2
self.executor.submit(self.update_tree, None, v, history)
break
with self.node_lock[state]:
if state not in self.tree:
# Expand and Evaluate
self.tree[state].sum_n = 1
self.tree[state].legal_moves = senv.get_legal_moves(state)
self.tree[state].waiting = True
# logger.debug(f"expand_and_evaluate {state}, sum_n = {self.tree[state].sum_n}, history = {history}")
if is_root_node and real_hist:
self.expand_and_evaluate(state, history, real_hist)
else:
self.expand_and_evaluate(state, history)
break
if state in history[:-1]: # loop
for i in range(len(history) - 1):
if history[i] == state:
if senv.will_check_or_catch(state, history[i + 1]):
self.executor.submit(self.update_tree, None,
-1, history)
elif senv.be_catched(state, history[i + 1]):
self.executor.submit(self.update_tree, None, 1,
history)
else:
# logger.debug(f"loop -> loss, state = {state}, history = {history[:-1]}")
self.executor.submit(self.update_tree, None, 0,
history)
break
break
# Select
node = self.tree[state]
if node.waiting:
node.visit.append(history)
# logger.debug(f"wait for prediction state = {state}")
break
sel_action = self.select_action_q_and_u(state, is_root_node)
virtual_loss = self.config.play.virtual_loss
self.tree[state].sum_n += 1
# logger.debug(f"node = {state}, sum_n = {node.sum_n}")
action_state = self.tree[state].a[sel_action]
action_state.n += virtual_loss
action_state.w -= virtual_loss
action_state.q = action_state.w / action_state.n
# logger.debug(f"apply virtual_loss = {virtual_loss}, as.n = {action_state.n}, w = {action_state.w}, q = {action_state.q}")
# if action_state.next is None:
history.append(sel_action)
state = senv.step(state, sel_action)
history.append(state)