def play(next_actions):
state = State()
while True:
if state.is_done():
break
action_idx = 0 if state.is_first_player() else 1
next_action = next_actions[action_idx]
action = next_action(state)
state = state.next_state(action)
return first_player_point(state)
def play(model):
history = []
state = State()
while True:
if state.is_done():
break
scores = pv_mcts_scores(model, state, SP_TEMPERATURE)
# Add state and policy into history
policies = [0 for _ in range(DN_OUTPUT_SIZE)]
for action, policy in zip(state.legal_actions(), scores):
policies[action] = policy
history.append([[state.pieces, state.enemy_pieces], policies, None])
action = np.random.choice(state.legal_actions(), p=scores)
state = state.next_state(action)
value = first_player_value(state)
for i in range(len(history)):
history[i][2] = value
value = -value
return history
C_PUCT = 1.0
t = sum(nodes_to_scores(self.child_nodes))
pucb_values = [
(-child_node.w / child_node.n if child_node.n > 0 else 0.0) +
C_PUCT * child_node.policy * math.sqrt(t) / (1 + child_node.n)
for child_node in self.child_nodes
]
return self.child_nodes[np.argmax(pucb_values)]
def pv_mcts_action(model, temperature=0):
def pv_mcts_action(state):
scores = pv_mcts_scores(model, state, temperature)
return np.random.choice(state.legal_actions(), p=scores)
return pv_mcts_action
if __name__ == '__main__':
path = sorted(Path('./model').glob('*.h5'))[-1]
model = tf.keras.models.load_model(str(path))
state = State()
next_action = pv_mcts_action(model, 1.0)
while True:
if state.is_done():
break
action = next_action(state)
state = state.next_state(action)
print(state)
