def _playout(self, game_state: go.GameState):
"""Run a single playout from the root to the leaf, getting a value at
the leaf and propagating it back through its parents.
State is modified in-place, so a copy must be provided.
"""
node = self._root
while True:
if node.is_leaf():
break
action, node = node.select(self._c_puct)
if action == PASS_MOVE:
move = PASS_MOVE
else:
move = (action // game_state.size, action % game_state.size)
game_state.do_move(move)
if not game_state.is_end_of_game and len(game_state.get_legal_moves(False)) > 0:
action_probs, leaf_value = self._policy_value_net.policy_value_fn(
game_state
)
node.expand(action_probs)
else:
winner = game_state.get_winner()
# for end state，return the "true" leaf_value
if winner is None: # tie
leaf_value = 0.0
else:
leaf_value = 1.0 if winner == game_state.current_player else -1.0
# Update value and visit count of nodes in this traversal.
node.update_recursive(-leaf_value)
def _evaluate_rollout(self, game_state: go.GameState, limit=1000):
"""Use the rollout policy to play until the end of the game,
returning +1 if the current player wins, -1 if the opponent wins,
and 0 if it is a tie.
"""
player = game_state.current_player
for _ in range(limit):
if game_state.is_end_of_game or len(
game_state.get_legal_moves(False)) == 0:
break
action_probs = _rollout_policy_fn(game_state)
max_action = max(action_probs, key=itemgetter(1))[0]
if max_action == PASS_MOVE:
move = PASS_MOVE
else:
move = (max_action // game_state.size,
max_action % game_state.size)
game_state.do_move(move)
else:
# If no break from the loop, issue a warning.
print("WARNING: rollout reached move limit")
winner = game_state.get_winner()
if winner is None: # tie
return 0.0
else:
return 1.0 if winner == player else -1.0
