def update_value_map_random(value_map, player, move):
value_map[player, move] = 0
if not is_winner(player, move):
otherplayer = 3 - player
for next_move in next_states(otherplayer, move):
if (otherplayer, next_move) not in value_map:
update_value_map_random(value_map, otherplayer, next_move)
def player_move_good(player, state):
otherplayer = 3 - player
moves = next_states(player, state)
winning_states = [
next_state for next_state in moves if is_winner(player, next_state)
]
if winning_states:
return random.choice(winning_states)
moves_other = next_states(otherplayer, state)
blocking_states = [
next_state
for (next_state, next_state_other) in zip(moves, moves_other)
if is_winner(otherplayer, next_state_other)
]
if blocking_states:
return random.choice(blocking_states)
return moves[0]
def update_value_map_never_lose(value_map, player, move):
otherplayer = 3 - player
next_moves = next_states(otherplayer, move)
if is_winner(player, move):
value_map[player, move] = 1
elif not next_moves:
value_map[player, move] = 0
else:
# assume other player tries to maximize their value
for next_move in next_moves:
if (otherplayer, next_move) not in value_map:
update_value_map_never_lose(value_map, otherplayer, next_move)
max_value = max(value_map[otherplayer, next_move]
for next_move in next_moves)
# if other player wins, we lose, so our value is minus their value
value_map[player, move] = -max_value
def player_move_learner(player, state, value_map, verbose=True, explore=0.1):
# List all possible next moves
next_moves = next_states(player, state)
# List probabilities of winning corresponding to possible next moves
#(or 0.5 if no value assigned)
next_moves_values = [value_map.get(i, 0.5) for i in next_moves]
# Find the index of the move corresponding to the maximum value
maxpos = [
i for i, v in enumerate(next_moves_values)
if v == max(next_moves_values)
]
rand = random.random()
# Randomly explore sometimes
if rand < explore:
if verbose: print("\033[1;32;40m expoloratory move \n")
return random.choice(next_moves)
# Otherwise return best move
else:
return next_moves[maxpos[0]]
def update_value_map_good(value_map, player, move):
otherplayer = 3 - player
next_moves = next_states(otherplayer, move)
if is_winner(player, move):
value_map[player, move] = 1
elif not next_moves:
value_map[player, move] = 0
else:
if any(is_winner(otherplayer, next_move) for next_move in next_moves):
# bad score if other player can win in the next move
value_map[player, move] = -1
else:
# heuristic score which prefers center, corners, and edges
# in that order
scores = (.01, .001, .01, .001, .1, .001, .01, .001, .01)
value_map[player,
move] = sum(score for (val, score) in zip(move, scores)
if val == player)
for next_move in next_moves:
if (otherplayer, next_move) not in value_map:
update_value_map_good(value_map, otherplayer, next_move)
def get_value_map(update_func):
value_map = {}
initial_state = (0, 0, 0, 0, 0, 0, 0, 0, 0)
for move in next_states(1, initial_state):
update_func(value_map, 1, move)
return value_map
def player_move_random(player, state):
return random.choice(next_states(player, state))