def main_loop(self):
agent = Agent()
numGames = 0
top = 0
while numGames < 100:
food = Food(self.size, self.screen)
food.food_new()
snake = Snake(size=self.size)
while not self.over:
agent.epsilon = 100 - numGames
oldState = agent.get_state(snake, food)
if randint(0, 200) < agent.epsilon:
move = to_categorical(randint(0, 2),
num_classes=3,
dtype='int32')
else:
predict = agent.model.predict(oldState.reshape(1, 11))
move = to_categorical(np.argmax(predict[0]),
num_classes=3,
dtype='int32')
if np.array_equal(move, [1, 0, 0]):
snake.xVel = 10
print('condition1')
elif np.array_equal(
move,
[0, 1, 0]) and snake.yVel == 0: # right - going horizontal
snake.yVel = 10
print('condition2')
elif np.array_equal(
move,
[0, 1, 0]) and snake.xVel == 0: # right - going vertical
snake.xVel = 10
print('condition3')
elif np.array_equal(
move,
[0, 0, 1]) and snake.yVel == 0: # left - going horizontal
snake.yVel = -10
print('condition4')
elif np.array_equal(
move,
[0, 0, 1]) and snake.xVel == 0: # left - going vertical
snake.xVel = -10
print('condition5')
snake.snake_move()
self.check_collisions(snake, food)
self.update_window(snake, food)
self.clock.tick(10)
newState = agent.get_state(snake, food)
reward = agent.get_reward(self.foodCollide, self.over)
agent.train_short(oldState, move, reward, newState, self.over)
agent.write_memory(oldState, move, reward, newState, self.over)
agent.replay(agent.mem)
numGames += 1
print(numGames)
agent = Agent(gamma=0.99,
epsilon=1.0,
alpha=0.00025,
input_dims=env.observation_space.shape,
n_actions=env.action_space.n,
mem_size=200_000,
eps_min=0.1,
batch_size=32,
replace=10_000,
eps_dec=1e-5,
save_name='dqn_model',
load_name='dqn_model_5000it.h5')
if load_checkpoint:
agent.epsilon = 0.1
agent.load_models()
last_ep = 0
for episode in tqdm(range(num_games)):
done = False
observation = env.reset()
score = 0
while not done:
action = agent.choose_action(observation)
observation_, reward, done, info = env.step(action)
n_steps += 1
score += reward
if not render:
agent.store_transition(observation, action, reward,
observation_, int(done))
