-
Notifications
You must be signed in to change notification settings - Fork 1
/
nec_agent.py
891 lines (738 loc) · 42.6 KB
/
nec_agent.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
import logging
import sys
import os
import argparse
from collections import deque, OrderedDict
import numpy as np
from scipy.signal import lfilter
import tensorflow as tf
import tensorflow.contrib.slim as slim
# from tensorflow.python.client import timeline
from lru import LRU
from pyflann import FLANN
from mmh3 import hash128
from replay_memory import ReplayMemory
log = logging.getLogger(__name__)
parser = argparse.ArgumentParser()
parser.add_argument("--gpu_id", help="The id of GPU (default 0)")
args = vars(parser.parse_args())
os.environ["CUDA_VISIBLE_DEVICES"] = args["gpu_id"] if args["gpu_id"] else "0"
class NECAgent:
def __init__(self, action_vector, cpu_only=False, dnd_max_memory=500000, neighbor_number=50,
backprop_learning_rate=1e-4, tabular_learning_rate=1e-3, fully_conn_neurons=128,
input_shape=(84, 84, 4), kernel_size=((3, 3), (3, 3), (3, 3), (3, 3)), num_outputs=(32, 32, 32, 32),
stride=((2, 2), (2, 2), (2, 2), (2, 2)), delta=1e-3, rep_memory_size=1e5, batch_size=32,
n_step_horizon=100, discount_factor=0.99, log_save_directory=None, epsilon_decay_bounds=(5000, 25000),
optimization_start=1000, ann_rebuild_freq=10):
# TÖRÖLNI
self.seen_states_number = 0
self._cpu_only = cpu_only
# ----------- HYPERPARAMETERS ----------- #
self.delta = delta
self.initial_epsilon = 1
self.epsilon_decay_bounds = epsilon_decay_bounds
# Optimizer parameters
self.adam_learning_rate = backprop_learning_rate
self.batch_size = batch_size
self.optimization_start = optimization_start
# Tabular parameters
self.tab_alpha = tabular_learning_rate
self.dnd_max_memory = int(dnd_max_memory)
# Reinforcement learning parameters
self.n_step_horizon = n_step_horizon
self.discount_factor = discount_factor
# Convolutional layer parameters
self._input_shape = input_shape
self.fully_connected_neuron = fully_conn_neurons
self._kernel_size = kernel_size
self._stride = stride
self._num_outputs = num_outputs
# Environment specific parameters
self.action_vector = action_vector
self.number_of_actions = len(action_vector)
self.frame_stacking_number = input_shape[-1]
# ANN Search
self.ann_rebuild_freq = ann_rebuild_freq
self.neighbor_number = neighbor_number
self.anns = {k: AnnSearch(neighbor_number, dnd_max_memory, k) for k in action_vector}
# Replay memory
self.replay_memory = ReplayMemory(size=rep_memory_size, stack_size=input_shape[-1])
#AZ LRU az tf_index:state_hash mert az ann_search alapján kell a sorrendet updatelni mert a dict1-ben
# updatelni kell dict1 az state_hash:tf_index ez ahhoz kell hogy megnezzem hogy benne van-e tehát milyen
# legyen a tab_update és hogy melyik indexre a DND-ben
self.tf_index__state_hash = {k: LRU(self.dnd_max_memory) for k in action_vector}
self.state_hash__tf_index = {k: {} for k in action_vector}
# Tensorflow Session object
self.session = self._create_tf_session()
# Step numbers
self.global_step = 0
self.episode_step = 0
self.episode_number = 0
# For logging the total loss and windowed average episode reward
self.create_list_for_total_losses = True
self.episode_total_reward = 0
self.windowed_average_total_reward = deque(maxlen=15)
# ----------- TENSORFLOW GRAPH BUILDING ----------- #
self.dnd_placeholder_ops = OrderedDict()
self.dnd_key_gather_ops = OrderedDict()
self.dnd_value_gather_ops = OrderedDict()
self.dnd_scatter_update_placeholder_ops = OrderedDict()
self.dnd_scatter_update_key_ops = OrderedDict()
self.dnd_scatter_update_value_ops = OrderedDict()
self.dnd_value_update_placeholder_ops = OrderedDict()
self.dnd_key_ops, self.dnd_value_ops = OrderedDict(), OrderedDict()
if self._cpu_only:
device = "/cpu:0"
else:
device = "/device:GPU:0"
with tf.device(device):
self.state = tf.placeholder(shape=[None, *self._input_shape], dtype=tf.float32, name="state")
# Always better to use smaller kernel size! These layers are from OpenAI
# Learning Atari: An Exploration of the A3C Reinforcement
# TODO: USE 1x1 kernels-bottleneck, CS231n Winter 2016: Lecture 11 from 29 minutes
self.convolutional_layers = self._create_conv_layers()
# This is the final fully connected layer
self.state_embedding = slim.fully_connected(slim.flatten(self.convolutional_layers[-1]),
self.fully_connected_neuron, activation_fn=tf.nn.elu)
self._create_dnd_variables()
self._create_scatter_update_ops()
self._create_gather_ops()
self.nn_state_embeddings, self.nn_state_values = self._create_stacked_gather()
# DND calculation
# expand_dims() is needed to subtract the key(s) (state_embedding) from neighboring keys (Eq. 5)
self.expand_dims = tf.expand_dims(tf.expand_dims(self.state_embedding, axis=1), axis=1)
self.square_diff = tf.square(self.expand_dims - self.nn_state_embeddings)
# We clip the values here, because the 0 values cause problems during backward pass (NaNs)
self.distances = tf.sqrt(tf.clip_by_value(tf.reduce_sum(self.square_diff, axis=3),
1e-12, 1e12)) + self.delta
self.weightings = 1.0 / self.distances
# Normalised weightings (Eq. 2)
self.normalised_weightings = self.weightings / tf.reduce_sum(self.weightings, axis=2, keep_dims=True)
# (Eq. 1)
self.squeeze = tf.squeeze(self.nn_state_values, axis=3)
self.pred_q_values = tf.reduce_sum(self.squeeze * self.normalised_weightings, axis=2,
name="predicted_Q_values")
self.predicted_q = tf.argmax(self.pred_q_values, axis=1, name="predicted_Q_arg")
with tf.device("/cpu:0"):
# TODO: Check if action_index device placement is not a perf. problem (probably not)
# This has to be an iterable, e.g.: [1, 0, 0]
self.action_index = tf.placeholder(tf.int32, [None], name="action")
self.action_onehot = tf.one_hot(self.action_index, self.number_of_actions, axis=-1)
with tf.device(device):
# Loss Function
self.target_q = tf.placeholder(tf.float32, [None], name="target_Q")
self.q_value = tf.reduce_sum(tf.multiply(self.pred_q_values, self.action_onehot), axis=1,
name="calculated_Q_value")
self.td_err = tf.subtract(self.target_q, self.q_value, name="td_error")
self.total_loss = tf.square(self.td_err, name="total_loss")
# Optimizer
self.optimizer = tf.contrib.opt.LazyAdamOptimizer(self.adam_learning_rate).minimize(self.total_loss)
# ----------- AUXILIARY ----------- #
# ----------- TF related ----------- #
# Global initialization
with tf.device(device):
self.init_op = tf.global_variables_initializer()
self.session.run(self.init_op)
# Check op for NaN checking - if needed
# with tf.device(device):
# self.check_op = tf.add_check_numerics_ops()
# Saver op
self.saver = tf.train.Saver(max_to_keep=5)
# ----------- Episode related containers ----------- #
self._observation_list = []
self._agent_input_list = []
self._agent_input_hashes_list = []
self._agent_action_list = []
self._rewards_deque = deque()
self._q_values_list = []
# Logging and TF FileWriter
self.log_save_directory = log_save_directory
if self.log_save_directory:
self.summary_writer = tf.summary.FileWriter(self.log_save_directory, graph=self.session.graph)
self._log_hyperparameters()
# Create discount factor vector
self._gammas = list(map(lambda x: self.discount_factor ** x, range(self.n_step_horizon)))
# Create epsilon decay rate (Now it is linearly decreasing between 1 and 0.001)
self._epsilon_decay_rate = (1 - 0.001) / (self.epsilon_decay_bounds[1] - self.epsilon_decay_bounds[0])
# Majd kibasszuk innen
self.__options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
# This is the main function which we call in different environments during playing
def get_action(self, processed_observation):
# Get the agent input (frame-stacking) using the preprocessed observation
# We also store the relevant quantities
agent_input = self._get_agent_input(processed_observation)
# Get the action
action = self._get_action(agent_input)
# Optimize if the global_step number is above optimization_start (making sure we have enough elements in the
# replay memory and each DND)
if self.global_step >= self.optimization_start:
self._optimize()
# Calculate bootstrap Q value as early as possible, so we can insert the corresponding (S, A, Q) tuple into
# the replay memory. Because of this, the agent may sample from this example during the next _optimize()
# call. (Intentionally)
if len(self._rewards_deque) == self.n_step_horizon:
q = self._calculate_bootstrapped_q_value()
# Store (S, A, Q) in the replay memory
self._add_to_replay_memory(q)
# We pop the leftmost element from the rewards deque, hence the condition before
# _calculate_bootstrapped_q_value() remains True until the episode end.
# (Also we do not need this element anymore, since we have already used it for calculating the Q value.)
self._rewards_deque.popleft()
self.global_step += 1
self.episode_step += 1
return action
def get_action_for_test(self, processed_observation):
agent_input = self._get_agent_input(processed_observation)
action = self._get_action(agent_input)
self.episode_step += 1
return action
# This is the main function which we call in different environments after an episode is finished.
def update(self):
# játék vége van kiszámolom a disc_rewardokat viszont az elsőnek n_hor darab rewardból
# a másodiknak (n_hor-1) darab rewardból, a harmadiknak (n_hor-2) darab rewardból, ésígytovább.
# A bootstrap value itt mindig 0 tehát a Q(N) maga a discounted reward. Majd berakosgatom a replay memoryba
# Itt van lekezelve az, hogy a játék elején Monte-Carlo return-nel számoljuk ki a state-action value-kat.
q_ns = self._discount(self._rewards_deque)
self._add_to_replay_memory_episode_end(q_ns)
# DND Lengths before modification
dnd_lengths = self._dnd_lengths()
actions, batch_valid_indices, batch_indices_for_ann, state_embeddings, batch_cond_vector =\
self._tabular_like_update(self._agent_input_list, self._agent_input_hashes_list, self._agent_action_list,
self._q_values_list)
self._ann_index_update(actions, batch_valid_indices, batch_indices_for_ann, state_embeddings, batch_cond_vector,
dnd_lengths)
self.reset_episode_related_containers()
# Add average episode total reward to its deque
if self.log_save_directory:
self.windowed_average_total_reward.append(self.episode_total_reward)
self._tensorboard_reward_writer()
self.reset_episode_related_containers()
def reset_episode_related_containers(self):
self._observation_list = []
self._agent_input_list = []
self._agent_input_hashes_list = []
self._agent_action_list = []
self._rewards_deque = deque()
self._q_values_list = []
self.episode_step = 0
# Increment episode number
self.episode_number += 1
# Set episode total reward to 0
self.episode_total_reward = 0
def save_action_and_reward(self, a, r):
# Convert action to float here just for checking purposes. _check_list_ids()
self._agent_action_list.append(float(a))
self._rewards_deque.append(r)
# Add step reward to episode total reward
self.episode_total_reward += r
def agent_save(self, path):
self.saver.save(self.session, path + '/model_' + str(self.global_step) + '.cptk')
def full_save(self, path):
self.agent_save(path)
# az LRU mappán belül hozza létre az actionokhöz tartozó .npy fájlt.
# Ebből létre lehet hozni a "self.state_hash__tf_index" is!
try:
os.mkdir(path + '/LRU_' + str(self.global_step))
except FileExistsError:
pass
for a, dict in self.tf_index__state_hash.items():
np.save(path + '/LRU_' + str(self.global_step) + "/" + str(a) + '.npy', dict.items())
self.replay_memory.save(path, self.global_step)
def agent_load(self, path, glob_step_num):
self.saver.restore(self.session, path + "/model_" + str(glob_step_num) + '.cptk')
self.global_step = glob_step_num
def full_load(self, path, glob_step_num):
self.agent_load(path, glob_step_num)
for a in self.action_vector:
act_LRU = np.load(path + '/LRU_' + str(glob_step_num) + "/" + str(a) + '.npy')
# azért reversed, hogy a lista legelső elemét rakja bele utoljára, így az lesz az MRU
for tf_index, state_hash in reversed(act_LRU):
self.tf_index__state_hash[a][tf_index] = state_hash
self.state_hash__tf_index[a][state_hash] = tf_index
# ANN index building
dnd_keys = self.session.run(list(self.dnd_key_ops.values()))
for act, ann in self.anns.items():
action_index = self.action_vector.index(act)
ann.build_index(dnd_keys[action_index][:self._dnd_length(act)])
self.replay_memory.load(path, glob_step_num)
# Should be a pre-processed observation
def _get_agent_input(self, processed_observation):
if self.episode_step == 0:
agent_input = self._initial_frame_stacking(processed_observation)
else:
agent_input = self._frame_stacking(self._agent_input_list[-1], processed_observation)
# Saving the relevant quantities
self._observation_list.append(processed_observation)
self._agent_input_list.append(agent_input)
self._agent_input_hashes_list.append(hash128(agent_input))
# self._agent_input_hashes_list.append(hash(agent_input.tobytes()))
return agent_input
def _optimize(self):
# self.__run_metadata = tf.RunMetadata()
# Get the batches from replay memory and run optimizer
state_batch, action_batch, q_n_batch = self.replay_memory.get_batch(self.batch_size)
action_batch_indices = [self.action_vector.index(a) for a in action_batch]
search_keys = self.session.run(self.state_embedding,
feed_dict={self.state: state_batch})
batch_indices = self._search_ann(search_keys, 0)
feed_dict = {self.state: state_batch, self.action_index: action_batch_indices, self.target_q: q_n_batch}
feed_dict.update({o: k for o, k in zip(self.dnd_placeholder_ops.values(), batch_indices)})
batch_total_loss, _ = self.session.run([self.total_loss, self.optimizer],
feed_dict=feed_dict)
# options=self.__options, run_metadata=self.__run_metadata)
# self.summary_writer.add_run_metadata(self.__run_metadata, "run_data" + str(self.global_step))
# self.summary_writer.flush()
# Mean of the total loss for Tensorboard visualization
if self.log_save_directory:
self._tensorboard_loss_writer(batch_total_loss)
# log.debug("Optimizer has been run.")
# fetched_timeline = timeline.Timeline(self.__run_metadata.step_stats)
# chrome_trace = fetched_timeline.generate_chrome_trace_format()
# file = "/home/atoth/temp/lazy_adamopt_new_gather" + str(self.global_step) + ".json"
# with open(file, "w") as f:
# f.write(chrome_trace)
# print("bugyi")
def _get_action(self, agent_input):
# Choose the random action
if np.random.random_sample() < self.curr_epsilon():
action = np.random.choice(self.action_vector)
# Choose the greedy action
else:
# We expand the agent_input dimensions here to run the graph for batch_size = 1 -- action selection
search_keys = self.session.run(self.state_embedding,
feed_dict={self.state: np.expand_dims(agent_input, axis=0)})
batch_indices = self._search_ann(search_keys, 1)
feed_dict = {self.state: np.expand_dims(agent_input, axis=0)}
feed_dict.update({o: k for o, k in zip(self.dnd_placeholder_ops.values(), batch_indices)})
max_q = self.session.run(self.predicted_q, feed_dict=feed_dict)
log.debug("Max. Q value: {}".format(max_q[0]))
action = self.action_vector[max_q[0]]
log.debug("Chosen action: {}".format(action))
return action
def _tensorboard_loss_writer(self, batch_total_loss):
# if self.global_step == self.optimization_start:
if self.create_list_for_total_losses:
self.create_list_for_total_losses = False
self._loss_list = []
self._mean_size = 0
self._loss_list.append(batch_total_loss)
self._mean_size += 1
if self._mean_size % 10 == 0:
mean_total_loss = np.mean(self._loss_list)
summary = tf.Summary()
summary.value.add(tag='Total Loss', simple_value=float(mean_total_loss))
self.summary_writer.add_summary(summary, self.global_step)
self.summary_writer.flush()
self._loss_list = []
self._mean_size = 0
def _tensorboard_reward_writer(self):
average_windowd_episode_reward = np.mean(self.windowed_average_total_reward)
summary = tf.Summary()
summary.value.add(tag='Average episode reward', simple_value=float(average_windowd_episode_reward))
self.summary_writer.add_summary(summary, self.global_step)
self.summary_writer.flush()
def _add_to_replay_memory(self, q, episode_end=False):
s = self._observation_list[self.episode_step - self.n_step_horizon]
a = self._agent_action_list[self.episode_step - self.n_step_horizon]
# self._check_list_ids(s, a, q)
self.replay_memory.append((s, a, q), episode_end)
def _add_to_replay_memory_episode_end(self, q_list):
j = len(self._rewards_deque)
for i, (o, a, q_n) in enumerate(zip(self._observation_list[-j:], self._agent_action_list[-j:], q_list)):
self._q_values_list.append(q_n)
e_e = False
if i == j - 1:
e_e = True
# self._check_list_ids(o, a, q_n)
self.replay_memory.append((o, a, q_n), e_e)
# Note that this function calculate only one Q at a time.
def _calculate_bootstrapped_q_value(self):
discounted_reward = np.dot(self._rewards_deque, self._gammas)
state = [self._agent_input_list[self.episode_step]]
search_keys = self.session.run(self.state_embedding,
feed_dict={self.state: state})
batch_indices = self._search_ann(search_keys, 0)
feed_dict = {self.state: state}
feed_dict.update({o: k for o, k in zip(self.dnd_placeholder_ops.values(), batch_indices)})
bootstrap_value = np.amax(self.session.run(self.pred_q_values,
feed_dict=feed_dict))
disc_bootstrap_value = self.discount_factor ** self.n_step_horizon * bootstrap_value
q_value = discounted_reward + disc_bootstrap_value
# Store calculated Q value
self._q_values_list.append(q_value)
return q_value
def curr_epsilon(self):
eps = self.initial_epsilon
if self.epsilon_decay_bounds[0] <= self.global_step < self.epsilon_decay_bounds[1]:
eps = self.initial_epsilon - ((self.global_step - self.epsilon_decay_bounds[0]) * self._epsilon_decay_rate)
elif self.global_step >= self.epsilon_decay_bounds[1]:
eps = 0.001
return eps
def _search_ann(self, search_keys, update_LRU_order):
batch_indices = []
for act, ann in self.anns.items():
# These are the indices we get back from ANN search
indices = ann.query(search_keys)
# log.debug("ANN indices for action {}: {}".format(act, indices))
# Create numpy array with full of corresponding action vector index
# action_indices = np.full(indices.shape, self.action_vector.index(act))
# log.debug("Action indices for action {}: {}".format(act, action_indices))
# Riffle two arrays
# tf_indices = self._riffle_arrays(action_indices, indices)
batch_indices.append(indices)
# Very important part: Modify LRU Order here
# Doesn't work without tabular update of course!
if update_LRU_order == 1:
_ = [self.tf_index__state_hash[act][i] for i in indices.ravel()]
np_batch = np.asarray(batch_indices, dtype=np.int32)
# log.debug("Batch update indices: {}".format(np_batch))
# Reshaping to gather_nd compatible format
# final_indices = np.asarray([np_batch[:, j, :, :] for j in range(np_batch.shape[1])], dtype=np.int32)
return np_batch
def _tabular_like_update(self, states, state_hashes, actions, q_ns):
log.debug("Tabular like update has been started.")
# Making np arrays
states = np.asarray(states, dtype=np.float32)
q_ns = np.asarray(q_ns)
actions = np.asarray(actions, dtype=np.int32)
action_indices = np.asarray([self.action_vector.index(act) for act in actions])
dnd_q_values = np.zeros(q_ns.shape, dtype=np.float32)
dnd_gather_indices = np.asarray([self.state_hash__tf_index[a][sh] if sh in self.state_hash__tf_index[a]
else None for sh, a in zip(state_hashes, actions)])
# TÖRÖLNI
for i in dnd_gather_indices:
if i != None:
self.seen_states_number += 1
in_cond_vector = dnd_gather_indices != None
# indices = np.squeeze(self._riffle_arrays(action_indices[in_cond_vector], dnd_gather_indices[in_cond_vector]),
# axis=0)
indices = self._batches_by_action(action_indices[in_cond_vector], dnd_gather_indices[in_cond_vector])
feed_dict = {o: k for o, k in zip(self.dnd_placeholder_ops.values(), indices)}
dnd_q_vals = self.session.run(list(self.dnd_value_gather_ops.values()), feed_dict=feed_dict)
dnd_q_vals2 = [deque(np.squeeze(d, axis=1)) for d in dnd_q_vals]
dnd_q_vals = [dnd_q_vals2[a].popleft() for a in action_indices[in_cond_vector]]
dnd_q_values[in_cond_vector] = dnd_q_vals
local_sh_dict = {a: {} for a in self.action_vector}
# Batch means one complete game (21-points) in this context
batch_update_values = []
batch_indices = []
batch_states = []
batch_indices_for_ann = []
batch_valid_indices = np.full(q_ns.shape, False, dtype=np.bool)
batch_cond_vector = []
ii = 0
for j, (act, sh, q, state) in enumerate(zip(actions, state_hashes, q_ns, states)):
if sh in self.state_hash__tf_index[act] and sh not in local_sh_dict[act]:
update_value = self.tab_alpha * (q - dnd_q_values[j]) + dnd_q_values[j]
local_sh_dict[act][sh] = (ii, update_value)
# Add elements to lists
batch_states.append(state)
batch_indices.append(dnd_gather_indices[j])
batch_update_values.append(update_value)
batch_indices_for_ann.append(dnd_gather_indices[j])
batch_valid_indices[j] = True
# ANN related - Append True because it is already added to ANN points
batch_cond_vector.append(True)
ii += 1
elif sh in self.state_hash__tf_index[act] and sh in local_sh_dict[act]:
# We are not adding elements to the lists in this case
update_value = self.tab_alpha * (q - local_sh_dict[act][sh][1]) + local_sh_dict[act][sh][1]
ind = local_sh_dict[act][sh][0]
batch_update_values[ind] = update_value
local_sh_dict[act][sh] = (ind, update_value)
else:
if len(self.tf_index__state_hash[act]) < self.dnd_max_memory:
index = len(self.tf_index__state_hash[act])
else:
index, old_state_hash = self.tf_index__state_hash[act].peek_last_item()
del self.state_hash__tf_index[act][old_state_hash]
# LRU order stuff
self.tf_index__state_hash[act][index] = sh
self.state_hash__tf_index[act][sh] = index
# Add elements to lists and update local_sh_dict
local_sh_dict[act][sh] = (ii, q)
batch_states.append(state)
batch_indices.append(index)
batch_update_values.append(q)
batch_indices_for_ann.append(index)
batch_valid_indices[j] = True
batch_cond_vector.append(False)
ii += 1
batch_states = np.asarray(batch_states, dtype=np.float32)
batch_indices = np.expand_dims(np.asarray(batch_indices, dtype=np.int32), axis=1)
batch_update_values = np.asarray(batch_update_values, dtype=np.float32)
batch_indices_for_ann = np.asarray(batch_indices_for_ann, dtype=np.int32)
batch_cond_vector = np.asarray(batch_cond_vector, dtype=np.bool)
# Create batch indices and update values for TensorFlow session
# batch_indices = np.squeeze(self._riffle_arrays(action_indices[batch_valid_indices], batch_indices))
batch_states_mod = self._batches_by_action(action_indices[batch_valid_indices], batch_states, False)
batch_indices = self._batches_by_action(action_indices[batch_valid_indices], batch_indices, False)
batch_update_values = self._batches_by_action(action_indices[batch_valid_indices],
np.expand_dims(batch_update_values, axis=1), False)
# Batch tabular update
scatter_update_key_ops = list(self.dnd_scatter_update_key_ops.values())
scatter_update_value_ops = list(self.dnd_scatter_update_value_ops.values())
scatter_update_ph_ops = list(self.dnd_scatter_update_placeholder_ops.values())
scatter_update_value_ph_ops = list(self.dnd_value_update_placeholder_ops.values())
for i, (b_s, b_i, b_u) in enumerate(zip(batch_states_mod, batch_indices, batch_update_values)):
if len(b_s) > 0:
ops = [scatter_update_key_ops[i], scatter_update_value_ops[i]]
feed_dict = {self.state: b_s, scatter_update_ph_ops[i]: b_i, scatter_update_value_ph_ops[i]: b_u}
self.session.run(ops, feed_dict=feed_dict)
state_embeddings = self.session.run(self.state_embedding, feed_dict={self.state: batch_states})
log.debug("Tabular like update has been run.")
return actions, batch_valid_indices, batch_indices_for_ann, state_embeddings, batch_cond_vector
def _ann_index_update(self, actions, batch_valid_indices, batch_indices_for_ann, state_embeddings,
batch_cond_vector, dnd_lengths):
index_rebuild = not bool(self.episode_number % self.ann_rebuild_freq)
# FLANN Add point - every batch
if not index_rebuild:
for a in self.action_vector:
act_cond = actions[batch_valid_indices] == a
self.anns[a].update_ann(batch_indices_for_ann[act_cond], state_embeddings[act_cond],
batch_cond_vector[act_cond], dnd_lengths[self.action_vector.index(a)])
# FLANN index rebuild, if index_rebuild = True
if index_rebuild:
dnd_keys = self.session.run(list(self.dnd_key_ops.values()))
for act, ann in self.anns.items():
action_index = self.action_vector.index(act)
# Ez a jó (kövi sor)
ann.build_index(dnd_keys[action_index][:self._dnd_length(act)])
def _save_q_value(self, q):
self._q_values_list.append(q)
def _discount(self, x):
a = np.asarray(x)
return lfilter([1], [1, -self.discount_factor], a[::-1], axis=0)[::-1]
def _dnd_lengths(self):
return [len(self.tf_index__state_hash[a]) for a in self.action_vector]
def _dnd_length(self, a):
return len(self.tf_index__state_hash[a])
def _create_conv_layers(self):
"""
Create convolutional layers in the Tensorflow graph according to the hyperparameters, using Tensorflow slim
library.
Returns
-------
conv_layers: list
The list of convolutional operations.
"""
lengths_set = {len(o) for o in (self._num_outputs, self._kernel_size, self._stride)}
if len(lengths_set) != 1:
msg = "The lengths of the conv. layers params vector should be same. Lengths: {}, Vectors: {}".format(
[len(o) for o in (self._num_outputs, self._kernel_size, self._stride)],
(self._num_outputs, self._kernel_size, self._stride))
raise ValueError(msg)
conv_layers = []
inputs = [self.state]
for i, (num_out, kernel, stride) in enumerate(zip(self._num_outputs, self._kernel_size, self._stride)):
layer = slim.conv2d(activation_fn=tf.nn.elu, inputs=inputs[i], num_outputs=num_out,
kernel_size=kernel, stride=stride, padding='SAME')
conv_layers.append(layer)
inputs.append(layer)
return conv_layers
def _create_dnd_variables(self):
with tf.variable_scope("dnd_keys"):
for a in self.action_vector:
k = tf.get_variable("dnd_keys_for_action_" + str(a), (self.dnd_max_memory, self.fully_connected_neuron),
dtype=tf.float32, initializer=tf.zeros_initializer)
self.dnd_key_ops[a] = k
with tf.variable_scope("dnd_values"):
for a in self.action_vector:
v = tf.get_variable("dnd_values_for_action_" + str(a), (self.dnd_max_memory, 1),
dtype=tf.float32, initializer=tf.zeros_initializer)
self.dnd_value_ops[a] = v
def _create_gather_ops(self):
with tf.variable_scope("dnd_gather_ops"):
for a, k in self.dnd_key_ops.items():
self.dnd_placeholder_ops[a] = tf.placeholder(tf.int32, None, name="gather_ph_for_action_" + str(a))
self.dnd_key_gather_ops[a] = tf.gather(k, self.dnd_placeholder_ops[a], axis=0,
name="key_gather_op_for_action_" + str(a))
for a, v in self.dnd_value_ops.items():
self.dnd_value_gather_ops[a] = tf.gather(v, self.dnd_placeholder_ops[a], axis=0,
name="val_gather_op_for_action_" + str(a))
def _create_stacked_gather(self):
key_gather_ops = [op for op in self.dnd_key_gather_ops.values()]
value_gather_ops = [op for op in self.dnd_value_gather_ops.values()]
nn_state_embeddings = tf.stack(key_gather_ops, axis=1, name="nn_state_embeddings")
nn_state_values = tf.stack(value_gather_ops, axis=1, name="nn_state_values")
return nn_state_embeddings, nn_state_values
def _create_scatter_update_ops(self):
with tf.variable_scope("dnd_scatter_update"):
for a in self.action_vector:
self.dnd_scatter_update_placeholder_ops[a] = tf.placeholder(tf.int32, None,
name="update_ind_ph_for_action_" + str(a))
self.dnd_value_update_placeholder_ops[a] = tf.placeholder(tf.float32, None,
name="val_update_ph_for_action_" + str(a))
self.dnd_scatter_update_key_ops[a] = tf.scatter_nd_update(self.dnd_key_ops[a],
self.dnd_scatter_update_placeholder_ops[a],
self.state_embedding,
name="key_update_op_for_action_" + str(a))
self.dnd_scatter_update_value_ops[a] = tf.scatter_nd_update(self.dnd_value_ops[a],
self.dnd_scatter_update_placeholder_ops[a],
self.dnd_value_update_placeholder_ops[a],
name="val_update_op_for_action_" + str(a))
@staticmethod
def _riffle_arrays(array_1, array_2):
if len(array_1.shape) == 1:
array_1 = np.expand_dims(array_1, axis=0)
array_2 = np.expand_dims(array_2, axis=0)
tf_indices = np.empty([array_1.shape[0], array_1.shape[1] * 2], dtype=array_1.dtype)
# Riffle the action indices with ann output indices
tf_indices[:, 0::2] = array_1
tf_indices[:, 1::2] = array_2
return tf_indices.reshape((array_1.shape[0], array_1.shape[1], 2))
def _batches_by_action(self, array_1, array_2, use_deque=True):
indices = [deque() for _ in self.action_vector]
for a, i in zip(array_1, array_2):
indices[a].append(i)
if use_deque:
return indices
else:
return [np.asarray(array) for array in indices]
def _initial_frame_stacking(self, processed_obs):
return np.stack((processed_obs, ) * self.frame_stacking_number, axis=2)
@staticmethod
def _frame_stacking(s_t, o_t): # Ahol az "s_t" a korábban stackkelt 4 frame, "o_t" pedig az új observation
s_t1 = np.append(s_t[:, :, 1:], np.expand_dims(o_t, axis=2), axis=2)
return s_t1
def _log_hyperparameters(self):
log.info("The hyperparameters of the agent are:\n"
"Optimizer parameters\n"
"--------------------\n"
"Learning rate: {lr}\n"
"Batch size for optimization: {bs}\n"
"Global step number when optimization starts: {os}\n"
"\n"
"Tabular parameters\n"
"------------------\n"
"Q update learning rate: {qlr}\n"
"DND maximum memory: {dnd}\n"
"\n"
"Reinforcement learning parameters\n"
"---------------------------------\n"
"N-step horizon: {n}\n"
"Discount factor: {df}\n"
"Starting epsilon: {init_e}\n"
"Final epsilon: 0.001\n"
"Epsilon is linearly decaying between global step number {eps_d[0]} and {eps_d[1]}\n"
"\n"
"Convolutional layer parameters\n"
"------------------------------\n"
"Input shape: {inp_s}\n"
"Fully connected neuron number: {fcn}\n"
"Kernel sizes: {ks}\n"
"Strides: {ss}\n"
"Number of outputs of each layer: {num_o}\n"
"\n"
"Environment specific parameters\n"
"-------------------------------\n"
"Available actions: {act}\n"
"Frame stacking number: {fs}\n"
"\n"
"Approx. Nearest Neighbor search parameters\n"
"------------------------------------------\n"
"ANN update frequency(episode number): {auf}\n"
"Nearest Neighbor search number: {nn}".format(lr=self.adam_learning_rate, bs=self.batch_size,
os=self.optimization_start, qlr=self.tab_alpha,
dnd=self.dnd_max_memory, n=self.n_step_horizon,
df=self.discount_factor, init_e=self.initial_epsilon,
eps_d=self.epsilon_decay_bounds, inp_s=self._input_shape,
fcn=self.fully_connected_neuron, ks=self._kernel_size,
ss=self._stride, num_o=self._num_outputs,
act=self.action_vector, fs=self.frame_stacking_number,
auf=self.ann_rebuild_freq, nn=self.neighbor_number))
@staticmethod
def _create_tf_session():
# return tf.Session(config=tf.ConfigProto(log_device_placement=True))
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.85)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
return sess
def _check_list_ids(self, s, a, q):
def get_index(l, o):
for i, j in enumerate(l):
if id(j) == id(o):
return i
i_1 = get_index(self._observation_list, s)
i_2 = get_index(self._agent_action_list, a)
i_3 = get_index(self._q_values_list, q)
if not (i_1 == i_2 and i_2 == i_3):
raise ValueError("The indices are wrong: {}".format((i_1, i_2, i_3)))
class AnnSearch:
def __init__(self, neighbors_number, dnd_max_memory, action):
self.ann = FLANN()
self.neighbors_number = neighbors_number
self._ann_index__tf_index = {}
self._ann_index__tf_index_v2 = {}
self.dnd_max_memory = int(dnd_max_memory)
self._removed_points = 0
self.flann_params = None
# For logging purposes
self.action = action
def add_state_embedding(self, state_embedding):
self.ann.add_points(state_embedding)
def update_ann(self, tf_var_dnd_indices, state_embeddings, cond_vector, dnd_actual_length):
# A tf_var_dnd_index alapján kell törölnünk a Flann indexéből. Ez csak abban az esetben fog
# kelleni, ha nincs index build és egy olyan index jön be, amihez tartozó state_embeddeinget már egyszer hozzáadtam.
# Ha láttuk már a pontot akkor ki kell törölni, mert a state hash-hehz tartozó state embedding érték megváltozott
# és azt tároljuk ANN-ben
flann_indices_seen = []
for tf_var_dnd_index in tf_var_dnd_indices[cond_vector]:
if tf_var_dnd_index in self._ann_index__tf_index.values():
index = [k for k, v in self._ann_index__tf_index.items() if v == tf_var_dnd_index][0]
else:
index = tf_var_dnd_index
flann_indices_seen.append(index)
# flann_indices_seen = [k for k, v in self._ann_index__tf_index.items() if v in tf_var_dnd_indices[cond_vector]]
self.ann.remove_points(flann_indices_seen)
for i, tf_var_dnd_index in enumerate(tf_var_dnd_indices[cond_vector]):
self._ann_index__tf_index[dnd_actual_length + self._removed_points + i] = tf_var_dnd_index
# Itt adjuk hozzá a FLANN indexéhez a már látott state hash-hez
if len(state_embeddings[cond_vector]) != 0:
self.add_state_embedding(state_embeddings[cond_vector])
self._removed_points += len(flann_indices_seen)
# Ha nem láttuk és tele vagyunk
# debug2_list = []
counter = 0
#print(len(tf_var_dnd_indices[~cond_vector]))
for i, tf_var_dnd_index in enumerate(tf_var_dnd_indices[~cond_vector]):
if dnd_actual_length + i >= self.dnd_max_memory:
if tf_var_dnd_index in self._ann_index__tf_index.values():
index = [k for k, v in self._ann_index__tf_index.items() if v == tf_var_dnd_index][0]
else:
index = tf_var_dnd_index
# ez a rész itt még zsivány, nem fölfele
self.ann.remove_point(index)
self._ann_index__tf_index[dnd_actual_length + self._removed_points + counter] = tf_var_dnd_index
self._removed_points += 1
else:
self._ann_index__tf_index[dnd_actual_length + self._removed_points + counter] = tf_var_dnd_index
counter += 1
self.add_state_embedding(state_embeddings[~cond_vector])
self._ann_index__tf_index_v2.update(self._ann_index__tf_index)
def build_index(self, tf_variable_dnd):
self.flann_params = self.ann.build_index(tf_variable_dnd, algorithm="kdtree", target_precision=1)
self._ann_index__tf_index = {}
self._removed_points = 0
# log.info("ANN index has been rebuilt for action {}.".format(self.action))
self._ann_index__tf_index_v2 = {i: i for i in range(len(tf_variable_dnd))}
def query(self, state_embeddings):
indices, _ = self.ann.nn_index(state_embeddings, num_neighbors=self.neighbors_number,
checks=self.flann_params["checks"])
# tf_var_dnd_indices = [[self._ann_index__tf_index[j] if j in self._ann_index__tf_index else j for j in index_row]
# for index_row in indices]
int64_indices = np.asarray(indices, dtype=np.int64)
tf_var_dnd_indices = [[self._ann_index__tf_index_v2[j] for j in index_row] for index_row in int64_indices]
return np.asarray(tf_var_dnd_indices, dtype=np.int32)
def setup_logging(level=logging.INFO, is_stream_handler=True, is_file_handler=False, file_handler_filename=None):
log.setLevel(level)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
if is_stream_handler:
ch = logging.StreamHandler(sys.stdout)
ch.setLevel(level)
ch.setFormatter(formatter)
log.addHandler(ch)
if file_handler_filename:
fh = logging.FileHandler(file_handler_filename)
fh.setLevel(level)
fh.setFormatter(formatter)
log.addHandler(fh)