S = np.zeros(screen_width * screen_height, dtype=np.uint8)
S = S.reshape(screen_height, screen_width)[:84, :84]
this_counter = exp_replay.sent_counter
if this_counter > last_counter + 1000:
last_counter = this_counter
tot_m, used_m, free_m = os.popen(
"free -th").readlines()[-1].split()[1:]
# the first three entries should match til 1M steps
# then the second 2 should continue in lock step
print("{}: {}, {}; {}, {}, {}".format(
exp_replay.sent_counter, len(exp_replay.memory),
len(exp_replay.reverse_experience_lookup.keys()), tot_m,
used_m, free_m))
while not ale.game_over():
S_prime = np.zeros(screen_width * screen_height, dtype=np.uint8)
ale.getScreen(S_prime)
S_prime = S_prime.reshape(screen_height, screen_width)[:84, :84]
a = random_state.choice(len(legal_actions))
action = legal_actions[a]
# Apply an action and get the resulting reward
reward = ale.act(action)
won = 0
ongoing_flag = 1
experience = (S_prime, action, reward, won, ongoing_flag)
S = S_prime
exp_replay.add_experience(experience)
batch = exp_replay.get_minibatch()
batch = exp_replay.get_minibatch(index_list=[1, 2, 3, 10, 11])
#if batch is not None:
# mb_S = batch[0]
# mb_S_prime = batch[1]
def test_full_run():
from atari_py.ale_python_interface import ALEInterface
game = "atari_roms/breakout.bin"
ale = ALEInterface()
# Get & Set the desired settings
ale.setInt('random_seed', 123)
# Load the ROM file
ale.loadROM(game)
# Get the list of legal actions
legal_actions = ale.getLegalActionSet()
batch_size = 10
exp_replay = ReplayBuffer(batch_size)
(screen_width, screen_height) = ale.getScreenDims()
import os
tot_m, used_m, free_m = os.popen("free -th").readlines()[-1].split()[1:]
last_counter = 0
random_state = np.random.RandomState(218)
print("initial: {}, {}, {}".format(tot_m, used_m, free_m))
# Play 2k episodes
for episode in range(2000):
total_reward = 0
S = np.zeros(screen_width * screen_height, dtype=np.uint8)
S = S.reshape(screen_height, screen_width)[:84, :84]
this_counter = exp_replay.sent_counter
if this_counter > last_counter + 1000:
last_counter = this_counter
tot_m, used_m, free_m = os.popen(
"free -th").readlines()[-1].split()[1:]
# the first three entries should match til 1M steps
# then the second 2 should continue in lock step
print("{}: {}, {}; {}, {}, {}".format(
exp_replay.sent_counter, len(exp_replay.memory),
len(exp_replay.reverse_experience_lookup.keys()), tot_m,
used_m, free_m))
while not ale.game_over():
S_prime = np.zeros(screen_width * screen_height, dtype=np.uint8)
ale.getScreen(S_prime)
S_prime = S_prime.reshape(screen_height, screen_width)[:84, :84]
a = random_state.choice(len(legal_actions))
action = legal_actions[a]
# Apply an action and get the resulting reward
reward = ale.act(action)
won = 0
ongoing_flag = 1
experience = (S_prime, action, reward, won, ongoing_flag)
S = S_prime
exp_replay.add_experience(experience)
batch = exp_replay.get_minibatch()
batch = exp_replay.get_minibatch(index_list=[1, 2, 3, 10, 11])
if batch is not None:
mb_S = batch[0]
other_info = batch[1]
del batch
total_reward += reward
print 'Episode', episode, 'ended with score:', total_reward
ale.reset_game()
lst = 0
for i in range(10000):
if i > lst + 1000:
tot_m, used_m, free_m = os.popen(
"free -th").readlines()[-1].split()[1:]
print("POST MEM {}: {}, {}; {}, {}, {}".format(
exp_replay.sent_counter, len(exp_replay.memory),
len(exp_replay.reverse_experience_lookup.keys()), tot_m,
used_m, free_m))
lst = i
batch = exp_replay.get_minibatch()
mb_S = batch[0]
other_info = batch[1]
from IPython import embed
embed()
raise ValueError()