def test_soak(self):
state_shape = (50, 50, 6)
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())
def s_for(i):
return np.random.random(state_shape)
import random
i = 0
for e in xrange(10000):
# add an episode to rm
episode_len = random.choice([5, 7, 9, 10, 15])
initial_state = s_for(i)
action_reward_state = []
for i in range(i + 1, i + episode_len + 1):
a, r, s2 = (i * 10) + 7, (i * 10) + 8, s_for(i)
action_reward_state.append((a, r, s2))
rm.add_episode(initial_state, action_reward_state)
i += episode_len + 1
# dump
print rm.current_stats()
# fetch a batch, of all items, but do nothing with it.
_ = rm.batch(idxs=range(10))
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()
class TestReplayMemory(unittest.TestCase):
def setUp(self):
self.sess = tf.Session()
self.rm = ReplayMemory(self.sess,
buffer_size=3,
state_shape=(2, 3),
action_dim=2,
load_factor=2)
self.sess.run(tf.initialize_all_variables())
def assert_np_eq(self, a, b):
self.assertTrue(np.all(np.equal(a, b)))
def test_empty_memory(self):
# api
self.assertEqual(self.rm.size(), 0)
self.assertEqual(self.rm.random_indexes(), [])
b = self.rm.batch(4)
self.assertEqual(len(b), 5)
for i in range(5):
self.assertEqual(len(b[i]), 0)
# internals
self.assertEqual(self.rm.insert, 0)
self.assertEqual(self.rm.full, False)
def test_adds_to_full(self):
# add entries to full
initial_state = [[11, 12, 13], [14, 15, 16]]
action_reward_state = [(17, 18, [[21, 22, 23], [24, 25, 26]]),
(27, 28, [[31, 32, 33], [34, 35, 36]]),
(37, 38, [[41, 42, 43], [44, 45, 46]])]
self.rm.add_episode(initial_state, action_reward_state)
# api
self.assertEqual(self.rm.size(), 3)
# random_idxs are valid
idxs = self.rm.random_indexes(n=100)
self.assertEqual(len(idxs), 100)
self.assertEquals(sorted(set(idxs)), [0, 1, 2])
# batch returns values
# internals
self.assertEqual(self.rm.insert, 0)
self.assertEqual(self.rm.full, True)
# check state contains these entries
state = self.rm.sess.run(self.rm.state)
self.assertEqual(state[0][0][0], 11)
self.assertEqual(state[1][0][0], 21)
self.assertEqual(state[2][0][0], 31)
self.assertEqual(state[3][0][0], 41)
def test_adds_over_full(self):
def s_for(i):
return (np.array(range(1, 7)) + (10 * i)).reshape(2, 3)
# add one episode of 5 states; 0X -> 4X
initial_state = s_for(0)
action_reward_state = []
for i in range(1, 5):
a, r, s2 = (i * 10) + 7, (i * 10) + 8, s_for(i)
action_reward_state.append((a, r, s2))
self.rm.add_episode(initial_state, action_reward_state)
# add another episode of 4 states; 5X -> 8X
initial_state = s_for(5)
action_reward_state = []
for i in range(6, 9):
a, r, s2 = (i * 10) + 7, (i * 10) + 8, s_for(i)
action_reward_state.append((a, r, s2))
self.rm.add_episode(initial_state, action_reward_state)
# api
self.assertEqual(self.rm.size(), 3)
# random_idxs are valid
idxs = self.rm.random_indexes(n=100)
self.assertEqual(len(idxs), 100)
self.assertEquals(sorted(set(idxs)), [0, 1, 2])
# fetch a batch, of all items
batch = self.rm.batch(idxs=[0, 1, 2])
self.assert_np_eq(batch.reward, [[88], [68], [78]])
self.assert_np_eq(batch.terminal_mask, [[0], [1], [1]])
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()
def test_soak(self):
state_shape = (50, 50, 6)
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())
def s_for(i):
return np.random.random(state_shape)
import random
i = 0
for e in xrange(10000):
# add an episode to rm
episode_len = random.choice([5, 7, 9, 10, 15])
initial_state = s_for(i)
action_reward_state = []
for i in range(i + 1, i + episode_len + 1):
a, r, s2 = (i * 10) + 7, (i * 10) + 8, s_for(i)
action_reward_state.append((a, r, s2))
rm.add_episode(initial_state, action_reward_state)
i += episode_len + 1
# dump
print rm.current_stats()
# fetch a batch, of all items, but do nothing with it.
_ = rm.batch(idxs=range(10))
