def play_single_game():
"""Play a single game using the latest model snapshot"""
game = Game()
state_size = 16
debug = True
model = load_model(path + "/data/checkpoint")
game.new_game()
state = game.state()
state = np.reshape(state, [1, state_size])
while not game.game_over():
# get action from highest q-value
act_values = model.predict(state)
if len(game.available_actions()) < 4:
temp = game.available_actions()
for i in range(0, 4):
if i not in temp:
act_values[0][i] = -100
#returns action with highest q-value
action = np.argmax(act_values[0])
reward = (game.do_action(action))**2
next_state = game.state()
actions_available = game.available_actions()
if len(actions_available) == 0:
done = True
else:
done = False
next_state = np.reshape(next_state, [1, state_size])
state = next_state
print("Action:", ACTION_NAMES[action])
print("Reward:", reward)
game.print_state()
if done:
states = game.state()
states = np.reshape(state, [1, state_size])
max_value = np.amax(states[0])
print("Score:", game.score())
print("Max Value: " + str(2**max_value))
print("Game over.")
break
def save(self, name):
self.model.save(name)
if __name__ == "__main__":
game = Game()
agent = DQNAgent()
# agent.load("./save/file")
done = False
batch_size = agent.batch_size
debug = False
save_maxvalues = True
output_list = []
for e in range(EPISODES):
game.new_game()
state = game.state()
state = np.reshape(state, [1, agent.state_size])
while not game.game_over():
action = agent.act(state)
reward = (game.do_action(action))**2
if (agent.is_max_value_reward):
reward = 0
temp = game.state()
temp_reshaped = np.reshape(temp, [1, agent.state_size])
temp_max_value = np.amax(temp_reshaped[0])
if temp_max_value > agent.max_value_reward_threshold:
reward = agent.max_value_reward_amount
next_state = game.state()
actions_available = game.available_actions()
if len(actions_available) == 0: