def action(self, state, player=1):
if random.random() < self.epsilon:
move = c4.getRandomMove(state)
else:
move = self.greedy(state)
new_state = c4.makeMove(state, player, move)
self.previous_state = new_state
return move
def action(self, state, player=1):
if random.random() < self.epsilon:
move = c4.getRandomMove(state)
else:
move = self.greedy(state)
new_state = c4.makeMove(state, player, move)
self.previous_state = new_state
return move
def action(self, state):
r = random.random()
if r < self.epsilon:
move = self.random(state)
else:
move = self.greedy(state)
new_state = c4.makeMove(state, self.player, move)
self.prevstate = self.state_string(new_state)
self.prevscore = self.lookup(new_state)
return move
def action(self, state):
r = random.random()
if r < self.epsilon:
move = self.random(state)
else:
move = self.greedy(state)
new_state = c4.makeMove(state, self.player, move)
self.prevstate = self.state_string(new_state)
self.prevscore = self.lookup(new_state)
return move
def greedy(self, state):
maxval = float('-inf')
maxmove = None
for i in range(7):
if c4.isValidMove(state, i):
new_state = c4.makeMove(state, self.player, i)
val = self.lookup(new_state)
if val > maxval:
maxval = val
maxmove = i
self.backup(maxval)
return maxmove
def greedy(self, state):
maxval = float('-inf')
maxmove = None
for i in range(7):
if c4.isValidMove(state, i):
new_state = c4.makeMove(state, self.player, i)
val = self.lookup(new_state)
if val > maxval:
maxval = val
maxmove = i
self.backup(maxval)
return maxmove
def greedy(self, state, player=1):
max_value = float("-inf")
next_move = None
# TODO: implemen get_possible_moves in c4
for move in range(7):
if c4.isValidMove(state, move):
new_state = c4.makeMove(state, player, move)
val = self.net.predict(c4.getNeuralInput(new_state).reshape(1, 126), batch_size=1)
if val > max_value:
max_value = val
next_move = move
self.backup(max_value)
return next_move
def play(self):
state = c4.getNewBoard()
player = 1
while not c4.isBoardFull(state):
if player == 1:
move = self.action(state, player)
else:
move = c4.getRandomMove(state)
state = c4.makeMove(state, player, move)
if c4.isWinner(state, player):
return player
player *= -1
return 0.5 # draw
def play(self):
state = c4.getNewBoard()
player = 1
while not c4.isBoardFull(state):
if player == 1:
move = self.action(state, player)
else:
move = c4.getRandomMove(state)
state = c4.makeMove(state, player, move)
if c4.isWinner(state, player):
return player
player *= -1
return 0.5 # draw
def play(agent1, agent2):
state = empty_state()
player = 1
while not c4.isBoardFull(state):
if player > 0:
move = agent1.action(state)
else:
move = agent2.action(state)
state = c4.makeMove(state, player, move)
winner = game_over(state)
player *= -1
if winner != EMPTY:
return winner
return winner
def play(agent1, agent2):
state = empty_state()
player = 1
while not c4.isBoardFull(state):
if player > 0:
move = agent1.action(state)
else:
move = agent2.action(state)
state = c4.makeMove(state, player, move)
winner = game_over(state)
player *= -1
if winner != EMPTY:
return winner
return winner
def greedy(self, state, player=1):
max_value = float("-inf")
next_move = None
# TODO: implemen get_possible_moves in c4
for move in range(7):
if c4.isValidMove(state, move):
new_state = c4.makeMove(state, player, move)
val = self.net.predict(c4.getNeuralInput(new_state).reshape(
1, 126),
batch_size=1)
if val > max_value:
max_value = val
next_move = move
self.backup(max_value)
return next_move
