def test_large_var(self):
### python replay_memory_test.py TestReplayMemory.test_large_var
s = StopWatch()
state_shape = (50, 50, 6)
s.reset()
rm = ReplayMemory(self.sess,
buffer_size=10000,
state_shape=state_shape,
action_dim=2,
load_factor=1.5)
self.sess.run(tf.initialize_all_variables())
print "cstr_and_init", s.time()
bs1, bs1i, bs2, bs2i = rm.batch_ops()
# build a simple, useless, net that uses state_1 & state_2 idxs
# we want this to reduce to a single value to minimise data coming
# back from GPU
added_states = bs1 + bs2
total_value = tf.reduce_sum(added_states)
def random_s():
return np.random.random(state_shape)
for i in xrange(10):
# add an episode to rm
episode_len = random.choice([5, 7, 9, 10, 15])
initial_state = random_s()
action_reward_state = []
for i in range(i + 1, i + episode_len + 1):
a, r, s2 = (i * 10) + 7, (i * 10) + 8, random_s()
action_reward_state.append((a, r, s2))
start = time.time()
s.reset()
rm.add_episode(initial_state, action_reward_state)
t = s.time()
num_states = len(action_reward_state) + 1
print "add_episode_time", t, "#states=", num_states, "=> s/state", t / num_states
i += episode_len + 1
# get a random batch state
b = rm.batch(batch_size=128)
s.reset()
x = self.sess.run(total_value,
feed_dict={
bs1i: b.state_1_idx,
bs2i: b.state_2_idx
})
print "fetch_and_run", x, s.time()
