class SnakeEnv(gym.Env):
"""Open AI Snake Environment"""
metadata = {'render.modes': ['human']}
def __init__(self, size, max_without_eating=300, mode='standard'):
super(SnakeEnv, self).__init__()
self.max_without_eating = max_without_eating
self.steps_without_apple = 0
if mode == 'standard':
self.action_space = spaces.Discrete(4)
elif mode == 'pov':
self.action_space = spaces.Discrete(3)
else:
raise ValueError('Uknown mode: ' + str(mode))
self.observation_space = spaces.Box(low=0,
high=1,
shape=(size[0], size[1], 3),
dtype=np.uint8)
self.game = SnakeGame(size, controls=mode)
def step(self, action):
self.game.update(action)
if self.game.ate_apple:
reward = 1
self.steps_without_apple = 0
elif self.game.snake.dead:
reward = -1
else:
reward = 0
self.steps_without_apple += 1
done = self.steps_without_apple > self.max_without_eating or self.game.snake.dead
return self.game.get_state(), reward, done, {}
def reset(self):
self.game.reset()
self.explored = np.zeros_like(self.explored)
self.steps_without_apple = 0
return self.game.get_state()
def render(self, mode='human', close=False):
if mode == 'human':
time.sleep(0.1)
self.game.render(mode=mode)
def seed(self, seed=None):
np.random.seed(seed)
from snake_game import SnakeGame
from agent import Agent
from Plot import plot
if __name__ == '__main__':
plot_scores = []
plot_mean_scores = []
total_score = 0
record = 0
agent = Agent()
game = SnakeGame()
#game_loop
while True:
# get state
state_old = game.get_state()
# get move
action = agent.get_action(state_old)
# perform move and get new state
reward, done, score = game.play_step(action)
state_new = game.get_state()
# train short memory
agent.train_short_memory(state_old, action, reward, state_new, done)
# remember
agent.remember(state_old, action, reward, state_new, done)
if done: #게임이 끝났을때