def _run_single_playout(self, parent_node: Node, pipe_id=0, depth=1, chosen_nodes=None):
"""
This function works recursively until a leaf or terminal node is reached.
It ends by back-propagating the value of the new expanded node or by propagating the value of a terminal state.
:param state: Current game-state for the evaluation. This state differs between the treads
:param parent_node: Current parent-node of the selected node. In the first expansion this is the root node.
:param depth: Current depth for the evaluation. Depth is increased by 1 for every recursive call
:param chosen_nodes: List of moves which have been taken in the current path.
For each selected child node this list is expanded by one move recursively.
:param chosen_nodes: List of all nodes that this thread has explored with respect to the root node
:return: -value: The inverse value prediction of the current board state. The flipping by -1 each turn is needed
because the point of view changes each half-move
depth: Current depth reach by this evaluation
mv_list: List of moves which have been selected
"""
# Probably is better to be refactored
# Too many arguments (6/5) - Too many local variables (27/15) - Too many branches (28/12) -
# Too many statements (86/50)
if chosen_nodes is None: # select a legal move on the chess board
chosen_nodes = []
node, move, child_idx = self._select_node(parent_node)
if move is None:
raise Exception("Illegal tree setup. A 'None' move was selected which shouldn't be possible")
# update the visit counts to this node
# temporarily reduce the attraction of this node by applying a virtual loss /
# the effect of virtual loss will be undone if the playout is over
parent_node.apply_virtual_loss_to_child(child_idx, self.virtual_loss)
# append the selected move to the move list
chosen_nodes.append(child_idx) # append the chosen child idx to the chosen_nodes list
if node is None:
state = GameState(deepcopy(parent_node.board)) # get the board from the parent node
state.apply_move(move) # apply the selected move on the board
# get the transposition-key which is used as an identifier for the board positions in the look-up table
transposition_key = state.get_transposition_key()
# check if the addressed fen exist in the look-up table
# note: It's important to use also the halfmove-counter here, otherwise the system can create an infinite
# feed-back-loop
key = transposition_key + (state.get_fullmove_number(),)
if self.use_transposition_table and key in self.node_lookup:
node = self.node_lookup[key] # get the node from the look-up list
# get the prior value from the leaf node which has already been expanded
value = node.initial_value
# clip the visit nodes for all nodes in the search tree except the director opp. move
clip_low_visit = self.use_pruning
new_node = Node(
node.board,
value,
node.policy_prob,
node.legal_moves,
node.is_leaf,
key,
clip_low_visit,
) # create a new node
with parent_node.lock:
parent_node.child_nodes[child_idx] = new_node # add the new node to its parent
else:
# expand and evaluate the new board state (the node wasn't found in the look-up table)
# its value will be back-propagated through the tree and flipped after every layer
my_pipe = self.my_pipe_endings[pipe_id] # receive a free available pipe
if self.send_batches:
my_pipe.send(state.get_state_planes())
# this pipe waits for the predictions of the network inference service
[value, policy_vec] = my_pipe.recv()
else:
state_planes = state.get_state_planes()
self.batch_state_planes[pipe_id] = state_planes
my_pipe.send(pipe_id)
result_channel = my_pipe.recv()
value = np.array(self.batch_value_results[result_channel])
policy_vec = np.array(self.batch_policy_results[result_channel])
is_leaf = is_won = False # initialize is_leaf by default to false and check if the game is won
# check if the current player has won the game
# (we don't need to check for is_lost() because the game is already over
# if the current player checkmated his opponent)
if state.is_check():
if state.is_loss():
is_won = True
# needed for e.g. atomic because the king explodes and is not in check mate anymore
if state.is_variant_loss():
is_won = True
if is_won:
value = -1
is_leaf = True
legal_moves = []
p_vec_small = None
# establish a mate in one connection in order to stop exploring different alternatives
parent_node.set_check_mate_node_idx(child_idx)
# get the value from the leaf node (the current function is called recursively)
# check if you can claim a draw - its assumed that the draw is always claimed
elif (
self.can_claim_threefold_repetition(transposition_key, chosen_nodes)
or state.get_pythonchess_board().can_claim_fifty_moves() is True
):
value = 0
is_leaf = True
legal_moves = []
p_vec_small = None
else:
legal_moves = state.get_legal_moves() # get the current legal move of its board state
if not legal_moves:
# stalemate occurred which is very rare for crazyhouse
if state.uci_variant == "giveaway":
value = 1
else:
value = 0
is_leaf = True
legal_moves = []
p_vec_small = None
# raise Exception("No legal move is available for state: %s" % state)
else:
try: # extract a sparse policy vector with normalized probabilities
p_vec_small = get_probs_of_move_list(
policy_vec, legal_moves, is_white_to_move=state.is_white_to_move(), normalize=True
)
except KeyError:
raise Exception("Key Error for state: %s" % state)
# clip the visit nodes for all nodes in the search tree except the director opp. move
clip_low_visit = self.use_pruning and depth != 1 # and depth > 4
new_node = Node(
state.get_pythonchess_board(),
value,
p_vec_small,
legal_moves,
is_leaf,
transposition_key,
clip_low_visit,
) # create a new node
if depth == 1:
# disable uncertain moves from being visited by giving them a very bad score
if not is_leaf and self.use_pruning:
if self.root_node_prior_policy[child_idx] < 1e-3 and value * -1 < self.root_node.initial_value:
with parent_node.lock:
value = 99
# for performance reasons only apply check enhancement on depth 1 for now
chess_board = state.get_pythonchess_board()
if self.enhance_checks:
self._enhance_checks(chess_board, legal_moves, p_vec_small)
if self.enhance_captures:
self._enhance_captures(chess_board, legal_moves, p_vec_small)
if not self.use_pruning:
self.node_lookup[key] = new_node # include a reference to the new node in the look-up table
with parent_node.lock:
parent_node.child_nodes[child_idx] = new_node # add the new node to its parent
elif node.is_leaf: # check if we have reached a leaf node
value = node.initial_value
else:
# get the value from the leaf node (the current function is called recursively)
value, depth, chosen_nodes = self._run_single_playout(node, pipe_id, depth + 1, chosen_nodes)
# revert the virtual loss and apply the predicted value by the network to the node
parent_node.revert_virtual_loss_and_update(child_idx, self.virtual_loss, -value)
# invert the value prediction for the parent of the above node layer because the player's changes every turn
return -value, depth, chosen_nodes
def evaluate_board_state(self, state: GameState): # Probably is better to be refactored
"""
Analyzes the current board state. This is the main method which get called by the uci interface or analysis
request.
:param state: Actual game state to evaluate for the MCTS
:return:
"""
# Too many local variables (28/15) - Too many branches (25/12) - Too many statements (75/50)
self.t_start_eval = time() # store the time at which the search started
if not self.net_pred_services[0].running: # check if the net prediction service has already been started
for net_pred_service in self.net_pred_services: # start the prediction daemon thread
net_pred_service.start()
legal_moves = state.get_legal_moves() # list of all possible legal move in the current board position
if not legal_moves: # consistency check
raise Exception("The given board state has no legal move available")
key = state.get_transposition_key() + (
state.get_fullmove_number(),
) # check first if the the current tree can be reused
if not self.use_pruning and key in self.node_lookup:
chess_board = state.get_pythonchess_board()
self.root_node = self.node_lookup[key] # if key in self.node_lookup:
if self.enhance_captures:
self._enhance_captures(chess_board, legal_moves, self.root_node.policy_prob)
# enhance checks for all direct child nodes
for child_node in self.root_node.child_nodes:
if child_node:
self._enhance_captures(child_node.board, child_node.legal_moves, child_node.policy_prob)
if self.enhance_checks:
self._enhance_checks(chess_board, legal_moves, self.root_node.policy_prob)
# enhance checks for all direct child nodes
for child_node in self.root_node.child_nodes:
if child_node:
self._enhance_checks(child_node.board, child_node.legal_moves, child_node.policy_prob)
logging.debug(
"Reuse the search tree. Number of nodes in search tree: %d",
self.root_node.nb_total_expanded_child_nodes,
)
self.total_nodes_pre_search = deepcopy(self.root_node.n_sum)
else:
logging.debug("Starting a brand new search tree...")
self.root_node = None
self.total_nodes_pre_search = 0
if len(legal_moves) == 1: # check for fast way out
max_depth_reached = 1 # if there's only a single legal move you only must go 1 depth
if self.root_node is None:
# conduct all necessary steps for fastest way out
self._expand_root_node_single_move(state, legal_moves)
# increase the move time buffer
# subtract half a second as a constant for possible delay
self.time_buffer_ms += max(self.movetime_ms - 500, 0)
else:
if self.root_node is None:
self._expand_root_node_multiple_moves(state, legal_moves) # run a single expansion on the root node
# opening guard
if state.get_fullmove_number() <= self.opening_guard_moves: # 100: #7: #10:
self.root_node.q_value[self.root_node.policy_prob < 5e-2] = -9999
# elif len(legal_moves) > 50:
# self.root_node.q_value[self.root_node.policy_prob < 1e-3] = -9999
# conduct the mcts-search based on the given settings
max_depth_reached = self._run_mcts_search(state)
t_elapsed = time() - self.t_start_eval
print("info string move overhead is %dms" % (t_elapsed * 1000 - self.movetime_ms))
# receive the policy vector based on the MCTS search
p_vec_small = self.root_node.get_mcts_policy(self.q_value_weight) # , xth_n_max=xth_n_max, is_root=True)
if self.use_future_q_values:
# use q-future value to update the q-values of direct child nodes
q_future, indices = self.get_last_q_values(min_nb_visits=5, max_depth=5) #25)
# self.root_node.q_value = 0.5 * self.root_node.q_value + 0.5 * q_future
# TODO: make this matrix vector form
if max_depth_reached >= 5:
for idx in indices:
self.root_node.q_value[idx] = min(self.root_node.q_value[idx], q_future[idx])
p_vec_small = self.root_node.get_mcts_policy(self.q_value_weight)
# if self.use_pruning is False:
self.node_lookup[key] = self.root_node # store the current root in the lookup table
best_child_idx = p_vec_small.argmax() # select the q-value according to the mcts best child value
value = self.root_node.q_value[best_child_idx]
# value = orig_q[best_child_idx]
lst_best_moves, _ = self.get_calculated_line()
str_moves = self._mv_list_to_str(lst_best_moves)
node_searched = int(self.root_node.n_sum - self.total_nodes_pre_search) # show the best calculated line
time_e = time() - self.t_start_eval # In uci the depth is given using half-moves notation also called plies
if len(legal_moves) != len(p_vec_small):
raise Exception(
"Legal move list %s with length %s is incompatible to policy vector %s"
" with shape %s for board state %s and nodes legal move list: %s"
% (legal_moves, len(legal_moves), p_vec_small, p_vec_small.shape, state, self.root_node.legal_moves)
)
# define the remaining return variables
centipawns = value_to_centipawn(value)
depth = max_depth_reached
nodes = node_searched
time_elapsed_s = time_e * 1000
# avoid division by 0
if time_e > 0.0:
nps = node_searched / time_e
else:
# return a high constant in otherwise
nps = 999999999
pv = str_moves
if self.verbose:
score = "score cp %d depth %d nodes %d time %d nps %d pv %s" % (
centipawns,
depth,
nodes,
time_elapsed_s,
nps,
pv,
)
logging.info("info string %s", score)
return value, legal_moves, p_vec_small, centipawns, depth, nodes, time_elapsed_s, nps, pv