diff = diff[tim_sux:chris_sux,:,0]
indices = np.where(diff == 200)
y = np.mean(indices[0]) + tim_sux
x = np.mean(indices[1]) # chris_sux
return (x,y)
env = gym.make('Breakout-v0')
learner = QLearner(num_states=500, num_actions=env.action_space.n)
for i_episode in range(2000):
observation = env.reset()
action = learner.set_initial_state(0)
prev = observation
total_reward = 0
for t in range(10000):
# env.render()
prev = observation
observation, reward, done, info = env.step(action)
total_reward += reward
paddle = find_paddle(observation)
x,y = find_ball(prev, observation)
try:
feature = int(paddle - x)
action = learner.move(feature, reward)
except ValueError:
feature = 250
action = learner.move(feature, reward, force_random=True)
if done:
print("Episode finished after {} timesteps. {} reward".format(t+1, total_reward))
break
env = gym.make('Breakout-v0')
learner = QLearner(num_states=200, num_actions=env.action_space.n)
for i_episode in range(2000):
observation = env.reset()
action = learner.set_initial_state(0)
prev = observation
for t in range(10000):
env.render()
# print(observation)
paddle = find_paddle(observation)
x,y = find_ball(prev, observation)
try:
feature = int(paddle - x)
if feature > 15:
feature = 15
if feature < -15:
feature = -15
except ValueError:
action = env.action_space.sample()
feature = 100
# import pdb; pdb.set_trace()
# action = env.action_space.sample()
prev = observation
observation, reward, done, info = env.step(action)
print feature, action
action = learner.move(feature, reward)
if done:
print("Episode finished after {} timesteps".format(t+1))
break
