-
Notifications
You must be signed in to change notification settings - Fork 15
/
coma_partially_centralized.py
589 lines (467 loc) · 26.9 KB
/
coma_partially_centralized.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
# This code was adapted from work done by GitHub user 'afzal63'
# a link to the specific peice of work used is listed below
# (https://github.com/PacktPublishing/Hands-On-Reinforcement-Learning-with-TensorFlow-TRFL/blob/master/Section%203/Actor-Critic.ipynb)
import tensorflow as tf
import sys
import numpy as np
import tensorflow_probability as tfp
import trfl
import matplotlib.pyplot as plt
import D2D_env_discrete as D2D
import statistics as stats
import collections
import json
# instantiate the simulator
ch = D2D.Channel()
# Actor Network Class
class ActorNetwork:
def __init__(self, name, obs_size=2, actor_hidden_size=32, actor_learning_rate=0.001):
with tf.variable_scope(name, "Actor"):
# define inputs placeholders for the actor network
self.name=name
self.input_ = tf.placeholder(tf.float32, [None, obs_size], name='inputs')
self.action_ = tf.placeholder(tf.int32, [None, 1], name='action_RB')
self.action_values_ = tf.placeholder(tf.float32, [None, 1], name='action_values')
# set up actor network
self.fc1_actor_ = tf.contrib.layers.fully_connected(self.input_, actor_hidden_size, activation_fn=tf.nn.elu)
self.fc2_actor_ = tf.contrib.layers.fully_connected(self.fc1_actor_, actor_hidden_size, activation_fn=tf.nn.elu)
self.fc3_actor_ = tf.contrib.layers.fully_connected(self.fc2_actor_, actor_hidden_size, activation_fn=tf.nn.elu)
self.fc4_actor_ = tf.contrib.layers.fully_connected(self.fc3_actor_, actor_hidden_size, activation_fn=tf.nn.elu)
self.fc5_actor = tf.contrib.layers.fully_connected(self.fc4_actor_, ch.n_actions, activation_fn=None)
# reshape the policy logits
self.policy_logits = tf.reshape(self.fc5_actor, (-1, 1, ch.n_actions))
# generate action probabilities for taking actions
self.action_prob = tf.nn.softmax(self.fc5_actor)
# get actor loss
self.Actor_return = trfl.discrete_policy_gradient(self.policy_logits, self.action_, self.action_values_)
# Optimize the loss
self.ac_loss = tf.reduce_mean(self.Actor_return)
self.ac_optim = tf.train.AdamOptimizer(learning_rate=actor_learning_rate).minimize(self.ac_loss)
# COMA critic network
class CriticNetwork:
def __init__(self, name, obs_size=None, action_size=None, critic_hidden_size=32, critic_learning_rate = 0.0001):
with tf.variable_scope(name, "Critic"):
# define inputs for COMA critic network
self.name=name
self.state_ = tf.placeholder(tf.float32, [None, obs_size], name='state')
self.joint_action_min_a = tf.placeholder(tf.float32, [None, ch.N_D2D - 1], name='joint_actions_min_a')
self.current_actor = tf.placeholder(tf.float32, [None, 1], name='current_actor')
self.joint_action_tm1 = tf.placeholder(tf.float32, [None, ch.N_D2D], name='joint_action_tm1')
self.actions_ = tf.placeholder(tf.int32, [None, 1], name='c_actions')
self.reward_ = tf.placeholder(tf.float32, [None, 1], name='reward')
self.discount_ = tf.placeholder(tf.float32, [None, 1], name='discount')
self.bootstrap_ = tf.placeholder(tf.float32, [None, 1, action_size], name='bootstrap')
# concatenate inputs into a single structure
self.input_ = tf.concat([self.state_, self.joint_action_min_a, self.current_actor, self.joint_action_tm1], 1, name='input')
# set up COMA critic network (hidden layers)
self.fc1_critic_ = tf.contrib.layers.fully_connected(self.input_, critic_hidden_size, activation_fn=tf.nn.elu)
self.fc2_critic_ = tf.contrib.layers.fully_connected(self.fc1_critic_, critic_hidden_size, activation_fn=tf.nn.elu)
self.fc3_critic_ = tf.contrib.layers.fully_connected(self.fc2_critic_, critic_hidden_size, activation_fn=tf.nn.elu)
self.fc4_critic_ = tf.contrib.layers.fully_connected(self.fc3_critic_, critic_hidden_size, activation_fn=tf.nn.elu)
# set up COMA critic network (output layer)
self.action_values_ = tf.contrib.layers.fully_connected(self.fc4_critic_, action_size, activation_fn=None)
#reshape output to work with loss function
self.av_reshape_ = tf.reshape(self.action_values_, [-1, 1, action_size], name='av_reshape')
# get COMA critic loss
self.Critic_return = trfl.qlambda(self.av_reshape_, self.actions_, self.reward_, self.discount_, self.bootstrap_, lambda_=lambda_)
# Optimize the loss
self.critic_loss_ = tf.reduce_mean(self.Critic_return.loss)
self.critic_optim = tf.train.AdamOptimizer(learning_rate=critic_learning_rate).minimize(self.critic_loss_)
# used to pass parameters to target network
def get_network_variables(self):
return [t for t in tf.trainable_variables() if t.name.startswith(self.name)]
# individual critics class
class IndCriticNetwork:
def __init__(self, name, critic_hidden_size=32, critic_learning_rate = 0.0001):
with tf.variable_scope(name, "Critic"):
# define inputs for individual critic networks
self.name=name
self.input_ = tf.placeholder(tf.float32, [None, obs_size], name='inputs')
self.reward_ = tf.placeholder(tf.float32, [None, 1], name='reward')
self.discount_ = tf.placeholder(tf.float32, [None, 1], name='discount')
self.bootstrap_ = tf.placeholder(tf.float32, [None], name='bootstrap')
# set up individual critic network (hidden layers)
self.fc1_critic_ = tf.contrib.layers.fully_connected(self.input_, critic_hidden_size, activation_fn=tf.nn.elu)
self.fc2_critic_ = tf.contrib.layers.fully_connected(self.fc1_critic_, critic_hidden_size, activation_fn=tf.nn.elu)
self.fc3_critic_ = tf.contrib.layers.fully_connected(self.fc2_critic_, critic_hidden_size, activation_fn=tf.nn.elu)
# set up individual critic network (output layer)
self.baseline_ = tf.contrib.layers.fully_connected(self.fc3_critic_, 1, activation_fn=None)
# get individual critic loss
self.Critic_return, self.advantage = trfl.sequence_advantage_critic_loss(self.baseline_,
self.reward_, self.discount_, self.bootstrap_, lambda_=lambda_,
baseline_cost=baseline_cost)
# Optimize the loss
self.critic_loss_ = tf.reduce_mean(self.Critic_return.loss)
self.critic_optim = tf.train.AdamOptimizer(learning_rate=critic_learning_rate).minimize(self.critic_loss_)
# used to pass parameters to target network
def get_network_variables(self): # have to sort out this for centralised architecture
return [t for t in tf.trainable_variables() if t.name.startswith(self.name)]
# initialize replay buffer
critic_replay_buffer = []
actor_replay_buffer = []
# initialize replay buffer indicator
replay_time_step = False
# hyperparameters
train_episodes = 5000
discount = 0.99
replay_prob = 0.15
actor_hidden_size = 64
critic_hidden_size = 64
individual_critic_hidden_size = 32
actor_learning_rate = 0.001
critic_learning_rate = 0.001
individual_critic_learning_rate = 0.001
beta = 0.1
baseline_cost = 1 # not used
entropy_cost = 0.01
normalise_entropy = True
lambda_ = 1. # not used
# get action and state sizes respectively
action_size = ch.n_actions
obs_size = ch.N_CU
# reset tensorflow graphs
tf.reset_default_graph()
# instantiate COMA critic network
central_critic = CriticNetwork(name="Critic_Net", obs_size=obs_size, action_size=action_size, critic_hidden_size=critic_hidden_size,
critic_learning_rate=critic_learning_rate)
target_critic_net = CriticNetwork(name="arget_Critic_Net", obs_size=obs_size, action_size=action_size, critic_hidden_size=critic_hidden_size,
critic_learning_rate=critic_learning_rate)
target_critic_update_ops = trfl.update_target_variables(target_critic_net.get_network_variables(),
central_critic.get_network_variables(), tau=0.001)
print('Instantiated Critic Network')
# instantiate actor networks and individual critic networks
D2D_actor_nets = []
individual_central_critics = []
individual_target_critic_nets = []
individual_target_critic_update_ops = []
for i in range(0, ch.N_D2D):
individual_central_critics.append(IndCriticNetwork(name='individual_Critic_Net_{:.0f}'.format(i),critic_hidden_size=individual_critic_hidden_size,
critic_learning_rate=individual_critic_learning_rate))
individual_target_critic_nets.append(IndCriticNetwork(name='Target_Critic_Net_{:.0f}'.format(i),critic_hidden_size=individual_critic_hidden_size,
critic_learning_rate=individual_critic_learning_rate))
individual_target_critic_update_ops.append(trfl.update_target_variables(individual_target_critic_nets[i].get_network_variables(),
individual_central_critics[i].get_network_variables(), tau=0.001))
print('Instantiated Individual Critic Network {:.0f} of {:.0f}'.format(i+1, ch.N_D2D))
D2D_actor_nets.append(ActorNetwork(name='a_net_{:.0f}'.format(i), obs_size=obs_size, actor_hidden_size=actor_hidden_size,
actor_learning_rate=actor_learning_rate))
print('Instantiated Actor Network {:.0f} of {:.0f}'.format(i+1, ch.N_D2D))
print('\n')
# used for progress reports in console
stats_rewards_list = []
stats_every = 10
# initialize the simulator and get channel gains
g_iB, g_j, G_ij, g_jB, G_j_j, d_ij = ch.reset()
# set initial state to list of 0s (one for each Cellular User)
state = np.zeros(ch.N_CU)
# initialize joint action excluding agent 'a' as all zeros
joint_action_min_a = np.zeros(ch.N_D2D - 1)
# initialize current agent indicator
a = 0
# initialize joint action at t-1 as all zeros
joint_action_tm1 = np.zeros(ch.N_D2D)
# used in plot smoothing procesdure
def running_mean(x, N):
cumsum = np.cumsum(np.insert(x, 0, 0))
return (cumsum[N:] - cumsum[:-N]) / N
# start tensorflow session
with tf.Session() as sess:
# Initialize variables
sess.run(tf.global_variables_initializer())
total_reward, ep_length = 0, 0
total_loss_list, action_list, action_prob_list, bootstrap_list, individual_bootstrap_list = [], [], [], [], []
critic_total_loss = []
rewards_list = []
collision_var = 0
D2D_collision_probs = []
collisions = []
access_ratios = []
access_rates = []
avg_throughput = []
time_avg_throughput = []
action_sel_record = []
# begin training
for ep in range(1, train_episodes):
# determine whether or not to use replay buffer for this time step
if replay_prob > np.random.random_sample() and len(critic_replay_buffer) > 1:
print('replaying_timestep')
replay_time_step = True
else:
replay_time_step = False
critic_replay_buffer.append([])
actor_replay_buffer.append([])
# used to determine the collision probability
ch.collision_indicator = 0
# generate action probabilities (from policy net forward pass) and use these to sample an action
action_probs = []
power_levels = []
RB_selections = []
actions = []
for i in range(0, ch.N_D2D):
# get action probabilities from actor network for each agent
action_probs.append(sess.run(D2D_actor_nets[i].action_prob, feed_dict={D2D_actor_nets[i].input_: np.expand_dims(state,axis=0)}))
action_probs[i] = action_probs[i][0]
# make a random choice of action based on the action probabilities
actions.append(np.random.choice(np.arange(len(action_probs[i])), p=action_probs[i]))
power_levels.append(ch.action_space[actions[i]][0])
RB_selections.append(ch.action_space[actions[i]][1])
# get cellular user SINRs based on channel gains and action selections
CU_SINR = ch.CU_SINR_no_collision(g_iB, power_levels, g_jB, RB_selections)
# get the next state from the simulator
next_state = ch.state(CU_SINR)
# get the D2D SINR based on the next state, channel gains and action selections
D2D_SINR = ch.D2D_SINR_no_collision(power_levels, g_j, G_ij, G_j_j, RB_selections, next_state)
# get rewards from simulator
reward, net, individualist_reward, socialist_reward = ch.D2D_reward_no_collision(D2D_SINR, CU_SINR, RB_selections, d_ij)
# divide rewards by channel bandwidth (to simplify learning)
reward = reward / 10**10
net = net / 10**10
individualist_reward = [i / 10**10 for i in individualist_reward]
socialist_reward = socialist_reward / 10**10
# used for determining the collision probability
if ch.collision_indicator > 0:
collision_var += 1
collisions.append(ch.collision_indicator)
D2D_collision_probs.append(collision_var / ep)
# prepare the next state for input to networks
next_state = np.clip(next_state,-1.,1.)
# update the total reward counter
total_reward += net
# used for post-training plots
ep_length += 1
# get bootstrap values from target nets
if ep == train_episodes:
# placeholder bootstrap value for the terminal state
bootstrap_value = np.zeros((1,),dtype=np.float32)
else:
individual_bootstrap_values = []
target_values = []
for i in range(0, ch.N_D2D):
# get copy of actions list and pop 'i'th entry to get joint_action_min_a
actions_copy = actions.copy()
actions_copy.pop(i)
# get bootsrap values for individual critics
individual_bootstrap_values.append(sess.run(individual_target_critic_nets[i].baseline_, feed_dict={
individual_target_critic_nets[i].input_: np.expand_dims(next_state, axis=0)}))
# get bootstrap values for COMA critic
target_values.append( sess.run(target_critic_net.action_values_, feed_dict={
target_critic_net.state_: np.expand_dims(next_state, axis=0),
target_critic_net.joint_action_min_a: np.reshape(actions_copy, [-1, ch.N_D2D - 1]),
target_critic_net.current_actor: np.reshape(i, [-1, 1]),
target_critic_net.joint_action_tm1: np.reshape(joint_action_tm1, [-1, ch.N_D2D])
}) )
# critic forward passes
action_values = []
for i in range(0, ch.N_D2D):
# get copy of actions list and remove 'i'th entry to get joint_action_min_a
actions_copy = actions.copy()
actions_copy.pop(i)
# get action values from COMA critic
action_values.append( sess.run(central_critic.action_values_, feed_dict={
central_critic.state_: np.expand_dims(next_state, axis=0),
central_critic.joint_action_min_a: np.reshape(actions_copy, [-1, ch.N_D2D - 1]),
central_critic.current_actor: np.reshape(i, [-1, 1]),
central_critic.joint_action_tm1: np.reshape(joint_action_tm1, [-1, ch.N_D2D])
}) )
# used for post-training plots
total_losses = []
# get a sample from the critic replay buffer if this time step is being replayed
if replay_time_step == True:
replay_sample_ind = range(0, len(critic_replay_buffer) - 1)
replay_sample_ind = np.random.choice(replay_sample_ind)
replay_sample = critic_replay_buffer[replay_sample_ind]
# critic updates
for i in range(0, ch.N_D2D):
actions_copy = actions.copy()
actions_copy.pop(i)
# update each individual critic
_, total_loss_individual_critic = sess.run([individual_central_critics[i].critic_optim,
individual_central_critics[i].critic_loss_], feed_dict={
individual_central_critics[i].input_: np.expand_dims(state, axis=0),
individual_central_critics[i].reward_: np.reshape(individualist_reward[i], (-1, 1)),
individual_central_critics[i].discount_: np.reshape(discount, (-1, 1)),
individual_central_critics[i].bootstrap_: np.reshape(individual_bootstrap_values[i], (1,))
})
# update COMA critic
if replay_time_step == True:
# update COMA critic with sample from replay buffer if time step is being replayed
_, total_loss_critic = sess.run([central_critic.critic_optim,
central_critic.critic_loss_], feed_dict={
central_critic.state_: np.expand_dims(replay_sample[i][0], axis=0),
central_critic.joint_action_min_a: np.reshape(replay_sample[i][1], [-1, ch.N_D2D - 1]),
central_critic.current_actor: np.reshape(replay_sample[i][2], [-1, 1]),
central_critic.joint_action_tm1: np.reshape(replay_sample[i][3], [-1, ch.N_D2D]),
central_critic.actions_: np.reshape(replay_sample[i][4], (-1, 1)),
central_critic.reward_: np.reshape(replay_sample[i][5], (-1, 1)),
central_critic.discount_: np.reshape(replay_sample[i][6], (-1, 1)),
central_critic.bootstrap_: np.reshape(replay_sample[i][7], (-1, 1, action_size))
})
else:
# update critic normally
_, total_loss_critic = sess.run([central_critic.critic_optim,
central_critic.critic_loss_], feed_dict={
central_critic.state_: np.expand_dims(state, axis=0),
central_critic.joint_action_min_a: np.reshape(actions_copy, [-1, ch.N_D2D - 1]),
central_critic.current_actor: np.reshape(i, [-1, 1]),
central_critic.joint_action_tm1: np.reshape(joint_action_tm1, [-1, ch.N_D2D]),
central_critic.actions_: np.reshape(actions[i], (-1, 1)),
central_critic.reward_: np.reshape(socialist_reward, (-1, 1)),
central_critic.discount_: np.reshape(discount, (-1, 1)),
central_critic.bootstrap_: np.reshape(target_values, (-1, 1, action_size))
})
# add the inputs to the COMA critic update for this time step to the critic replay buffer if time step WAS NOT replayed
critic_replay_buffer[len(critic_replay_buffer)-1].append([state, actions_copy, i, joint_action_tm1, actions[i], socialist_reward, discount, target_values])
# used for debugging / post training plots
critic_total_loss.append(total_loss_critic)
# get sample from actor replay buffer if this time step is being replayed
if replay_time_step == True:
replay_sample = actor_replay_buffer[replay_sample_ind]
# perform actor network updates
for i in range(0, ch.N_D2D):
# initialize 'base' used in counterfactual baseline computation
base = 0
# get TD error from individual critics
seq_i_c_return_, individualist_advantage = sess.run([individual_central_critics[i].Critic_return, individual_central_critics[i].advantage], feed_dict={
individual_central_critics[i].input_: np.expand_dims(state, axis=0),
individual_central_critics[i].reward_: np.reshape(individualist_reward[i], (-1, 1)),
individual_central_critics[i].discount_: np.reshape(discount, (-1, 1)),
individual_central_critics[i].bootstrap_: np.reshape(individual_bootstrap_values[i], (1,))
})
if replay_time_step == True:
# update actor networks with sample from actor replay buffer if time step is being replayed
_, total_loss, seq_aac_return = sess.run([D2D_actor_nets[i].ac_optim,
D2D_actor_nets[i].ac_loss,
D2D_actor_nets[i].Actor_return], feed_dict={
D2D_actor_nets[i].input_: np.expand_dims(replay_sample[i][0], axis=0),
D2D_actor_nets[i].action_: np.reshape(replay_sample[i][1], (-1, 1)),
D2D_actor_nets[i].action_values_: np.reshape(replay_sample[i][2], (-1, 1))
})
else:
# get sum(Q(s, u) * pi(u|s))
for j in range(0, action_size):
base += action_probs[i][j] * action_values[i][0][j]
# remove current actor's 'Q(s, a) * pi(a|s)' from base
base = base - (action_values[i][0][actions[i]] * action_probs[i][actions[i]])
# subtract base from action value of current actor's action to get A(s,u)
adv = action_values[i][0][actions[i]] - base
# perform beta mix between TD error and A(s,u)
advantage = ((1 - beta) * individualist_advantage[0][0]) + (beta * adv)
# update actor networks with 'advantage' as baseline
_, total_loss, seq_aac_return = sess.run([D2D_actor_nets[i].ac_optim,
D2D_actor_nets[i].ac_loss,
D2D_actor_nets[i].Actor_return], feed_dict={
D2D_actor_nets[i].input_: np.expand_dims(state, axis=0),
D2D_actor_nets[i].action_: np.reshape(actions[i], (-1, 1)),
D2D_actor_nets[i].action_values_: np.reshape(advantage, (-1, 1))
})
# add the inputs to the actor update for this time step to the actor replay buffer if time step WAS NOT replayed
actor_replay_buffer[len(critic_replay_buffer)-1].append([state, actions[i], advantage])
# used for debugging / post training plots
total_losses.append(total_loss)
total_loss_list.append(np.mean(total_losses))
# update COMA critic target network with params from COMA critic
sess.run(target_critic_update_ops)
for i in range(0, ch.N_D2D):
# update individual target networks with params from respective individual critics
sess.run(individual_target_critic_update_ops[i])
# update joint action at t-1
joint_action_tm1 = actions
# update state
state = next_state
# ALL FOLLOWING CODE IS FOR DEBUGGING / POST-TRAINING PLOTS
a = list(ch.accessed_CUs)
if 2 in a:
a = a.index(2)
b = RB_selections.index(a)
else:
b = 0
accessed = []
throughput = []
for i in range(0, len(reward)):
if reward[i] > 0:
accessed.append(reward[i])
throughput.append(reward[i])
else:
throughput.append(0.0)
access_ratios.append(len(accessed) / len(reward))
access_rates.append(sum(access_ratios) / ep)
avg_throughput.append(sum(throughput))
time_avg_throughput.append(sum(avg_throughput)/ ep)
action_sel_record.append(actions)
if ep % stats_every == 0 or ep == 1:
print('Power Levels: ', power_levels)
print('RB Selections: ', RB_selections)
print('Accessed CUs: ', ch.accessed_CUs)
print('Rewards of agents: ', reward)
print('Number of Collisions: ', ch.collision_indicator)
print('||(Ep)isode: {}|| '.format(ep),
'Last net (r)eward: {:.3f}| '.format(net),
'Throughput: {:.3f}| '.format(sum(throughput)),
'crit (L)oss: {:.4f}|'.format(np.mean(critic_total_loss)),
'actor (L)oss: {:.4f}|'.format(np.mean(total_loss_list)))
stats_rewards_list.append((ep, total_reward, ep_length))
rewards_list.append(net)
if len(critic_replay_buffer) > 500:
critic_replay_buffer.pop(0)
if len(actor_replay_buffer) > 500:
actor_replay_buffer.pop(0)
eps, rews, lens = np.array(stats_rewards_list).T
smoothed_rews = running_mean(rewards_list, 100)
smoothed_col_probs = running_mean(D2D_collision_probs, 100)
smoothed_access_rates = running_mean(access_rates, 100)
smoothed_throughput = running_mean(time_avg_throughput, 100)
sr = open('COMApc{}Rew.json'.format(beta*10), 'w+')
json.dump(list(smoothed_rews), sr)
sc = open('COMApc{}Col.json'.format(beta*10), 'w+')
json.dump(list(smoothed_col_probs), sc)
sa = open('COMApc{}Acc.json'.format(beta*10), 'w+')
json.dump(list(smoothed_access_rates), sa)
st = open('COMApc{}Thr.json'.format(beta*10), 'w+')
json.dump(list(smoothed_throughput), st)
reward_fig = plt.figure()
plt.plot(eps[-len(smoothed_rews):], smoothed_rews)
plt.plot(eps, rewards_list, color='grey', alpha=0.3)
plt.xlabel('Time-slot')
plt.ylabel('Reward')
plt.show()
collision_prob_fig = plt.figure()
plt.ylim(0, 1)
plt.xlim(0, train_episodes)
plt.plot(eps[-len(smoothed_col_probs):], smoothed_col_probs)
plt.plot(eps, D2D_collision_probs, color='grey', alpha=0.3)
plt.xlabel('Time-slot')
plt.ylabel('D2D collision probability')
plt.show()
true_collisions_fig = plt.figure()
plt.ylim(0, ch.N_D2D)
plt.xlim(0, train_episodes)
plt.plot(eps, collisions)
plt.ylabel('Number of collisions')
plt.xlabel('Time-slot')
plt.show()
access_rate_fig = plt.figure()
plt.ylim(0, 1)
plt.xlim(0, train_episodes)
plt.plot(eps[-len(smoothed_access_rates):], smoothed_access_rates)
plt.plot(eps, access_rates, color='grey', alpha=0.3)
plt.xlabel('Time-slot')
plt.ylabel('D2D access rate')
plt.show()
time_avg_overall_thrghpt_fig = plt.figure()
plt.plot(eps[-len(smoothed_throughput):], smoothed_throughput)
plt.plot(eps, time_avg_throughput, color='grey', alpha=0.3)
plt.xlabel('Time-slot')
plt.ylabel('Time-averaged network throughput')
plt.show()
loss_plot = plt.figure()
plt.xlim(0, train_episodes)
plt.plot(eps[-len(critic_total_loss):], critic_total_loss, color='red')
plt.xlabel('Time-slot')
plt.ylabel('Critic Loss')
plt.show()
pow_sel_plot = plt.figure()
plt.xlim(0, train_episodes)
for i in range(0, ch.N_D2D):
plt.plot(eps[-len(action_sel_record):], [item[i] for item in action_sel_record])
plt.xlabel('Time-slot')
plt.ylabel('Action Selection')
plt.show()