def simulate_with_heuristic(state, max_states=50_000):
visited = set()
moveseq = []
i = 0
while True:
v = len(visited)
if i >= max_states:
return EndState(solved=False, visited=v, msg="exceeded max states")
if state in visited:
return EndState(solved=False, msg="revisited state", visited=v)
visited.add(state)
# Yan et al. Section 4 "Machine Play"
# 1. identify set of legal moves
# 2. select and execute a legal move
actions = yan_et_al_prioritized_actions(state)
if len(actions) == 0:
return EndState(solved=False, visited=v, msg="run out of actions")
action = actions[0]
moveseq.append(action)
state = play_move(state, action)
# 3. If all cards are on suit stacks, declare victory and terminate.
if state_is_win(state):
return EndState(solved=True, moveseq=moveseq, visited=v)
# 4. If new card configuration repeats a previous one, declare loss and terminate.
# 5. Repeat procedure.
i += 1
def endgame(fname, k):
"""
fname: path to a shootme solution fixture
k: number of moves *remaining* until shootme solution
:returns: {KlonState}
"""
state, moveseq = state_with_moveseq(fname)
while len(moveseq) > k:
state = play_move(state, moveseq.pop(0))
return state
def yan_et_al_rollout_1(state):
moves = get_legal_moves(state)
for move in moves:
new_state = play_move(state, move)
result = simulate_with_heuristic(new_state)
if result.solved:
return EndState(
solved=True,
msg="solved in rollout",
visited=result.visited,
moveseq=(move,) + tuple(result.moveseq),
)
# no optimal move: use the strategy as before
actions = yan_et_al_prioritized_actions(state)
if len(actions) == 0:
return None
return actions[0]
def make_move(state, move):
new_state = play_move(state, move)
return KlondikeNode(*new_state, parent=state)
def validate_move_seq(state, move_seq):
state = copy(state)
for move in move_seq:
state = play_move(state, move)
return state_is_win(state)
def play_move(self, move):
child = play_move(self, move)
return KlonNode(*child, parent=self, action=move)