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keithmgould/slow_training.py

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slow_training.py
import numpy as np
import datetime
import tensorflow as tf
class Policy():
def __init__(self, session):
self.session = session
self.actions = tf.placeholder(tf.float32)
self.advantages = tf.placeholder(tf.float32)
self.observations = tf.placeholder(tf.float32, shape=[None, 4])
hidden_mu = tf.contrib.layers.fully_connected(
inputs=self.observations,
num_outputs=400,
activation_fn=tf.nn.relu,
weights_initializer=tf.random_normal_initializer())
hidden_mu2 = tf.contrib.layers.fully_connected(
inputs=hidden_mu,
num_outputs=128,
activation_fn=tf.nn.relu,
weights_initializer=tf.random_normal_initializer())
mu = tf.contrib.layers.fully_connected(
inputs=hidden_mu2,
num_outputs=1,
activation_fn=None)
hidden_sigma = tf.contrib.layers.fully_connected(
inputs=self.observations,
num_outputs=400,
activation_fn=tf.nn.relu,
weights_initializer=tf.random_normal_initializer())
hidden_sigma2 = tf.contrib.layers.fully_connected(
inputs=hidden_sigma,
num_outputs=128,
activation_fn=tf.nn.relu,
weights_initializer=tf.random_normal_initializer())
sigma = tf.contrib.layers.fully_connected(
inputs=hidden_sigma2,
num_outputs=1,
activation_fn=None)
sigma = tf.nn.softplus(sigma) + 1e-5 # soft relu
normal_dist = tf.contrib.distributions.Normal(mu, sigma)
log_prob = normal_dist.log_prob(self.actions)
loss = -tf.reduce_sum(tf.multiply(log_prob, self.advantages))
loss -= 1e-1 * normal_dist.entropy()
optimizer = tf.train.RMSPropOptimizer(learning_rate=.1)
self.train_op = optimizer.minimize(loss)
sample = normal_dist.sample(name='sampler/sample')
self.action = tf.clip_by_value(sample, hparams['action_space_low'], hparams['action_space_high'], name='sampler/clip')
def observe_and_act(self, observation):
return self.session.run(self.action, feed_dict={self.observations: [observation]})
def train_nets(self, batch_observations, batch_actions, batch_advantages):
batch_feed = {
self.observations: batch_observations,
self.actions: batch_actions,
self.advantages: batch_advantages
}
self.session.run(self.train_op, feed_dict=batch_feed)
# pretend these came from running through some environment (like OpenAI Gym)
# for a few episodes, and now we are ready to train :)
# Note there are 780 observations here.
batch_observations = [[0. , 0. , 0.0434578, 0. ], [ 0.00217534, 0.13183855, 0.03915462, -0.26079893], [ 0.00211501, -0.00365607, 0.0397624 , 0.03683532], [ 0.00423275, 0.12834807, 0.03603958, -0.22562575], [ 0.00411684, -0.00702504, 0.03720629, 0.0707099 ], [ 0.00618089, 0.12509403, 0.03402336, -0.19290482], [ 0.0060126 , -0.01019929, 0.03571593, 0.10258006], [ 0.0080254 , 0.12198774, 0.03304762, -0.16171589], [ 0.00780652, -0.01326582, 0.0352478 , 0.13334465], [ 0.0097691 , 0.11894448, 0.0330833 , -0.13118169], [ 0.00950001, -0.01630849, 0.03578716, 0.16386996], [ 0.01141205, 0.1158812 , 0.03412984, -0.10044362], [ 0.01109178, -0.01941034, 0.03734757, 0.19501369], [ 0.01295156, 0.11271433, 0.03621503, -0.06863826], [ 0.01583659, 0.17484994, 0.03314468, -0.18608208], [0.01648914, 0.0395484 , 0.0349439 , 0.10904387], [ 0.01932277, 0.17173509, 0.03237649, -0.15560082], [0.01992445, 0.03646548, 0.03467305, 0.13918509], [ 0.02270745, 0.16866685, 0.03260059, -0.12560354], [0.0232584 , 0.03339066, 0.03539322, 0.16925018], [ 0.0259902 , 0.16556358, 0.03382164, -0.09524679], [0.02648919, 0.03024219, 0.03712302, 0.20008361], [ 0.02916785, 0.16234276, 0.03607234, -0.06367794], [ 0.03204676, 0.1744794 , 0.03480972, -0.07652214], [0.0326921 , 0.03911158, 0.0384265 , 0.21919888], [ 0.03551606, 0.17114943, 0.0377004 , -0.04400637], [ 0.04051737, 0.30310988, 0.03262963, -0.30731922], [ 0.04328457, 0.16770865, 0.03242704, -0.01227848], [ 0.04823296, 0.29990232, 0.02784104, -0.27793926], [0.05095034, 0.16468978, 0.02809026, 0.0151046 ], [ 0.05585206, 0.29707426, 0.02392434, -0.25248003], [0.05852536, 0.16201773, 0.02456654, 0.03892114], [ 0.06338554, 0.29455665, 0.02076786, -0.23022293], [0.06601939, 0.15962707, 0.02175535, 0.05984755], [ 0.07084215, 0.2922889 , 0.01828145, -0.21053903], [0.07344025, 0.15746018, 0.01957634, 0.07847803], [ 0.07822883, 0.29021725, 0.01639396, -0.19287173], [0.08079401, 0.15546578, 0.01796709, 0.09534127], [ 0.08555086, 0.28829333, 0.01505119, -0.17672141], [0.08808521, 0.15359741, 0.01688129, 0.1109153 ], [ 0.09281202, 0.28647292, 0.01421437, -0.16163155], [0.09531692, 0.15181226, 0.01628744, 0.12564102], [ 0.10001471, 0.28471464, 0.01385904, -0.14717597], [0.10249086, 0.15006988, 0.01616796, 0.13993454], [ 0.10716002, 0.28297892, 0.01397434, -0.13294682], [0.10960748, 0.14833109, 0.01651862, 0.15419903], [ 0.11424772, 0.28122687, 0.01456266, -0.11854325], [0.11666591, 0.14655694, 0.01734845, 0.16883594], [ 0.12127633, 0.27941906, 0.01563971, -0.1035601 ], [0.123664 , 0.1447075 , 0.01867994, 0.1842563 ], [ 0.128243 , 0.2775147 , 0.01723493, -0.08757649], [0.13134928, 0.18825994, 0.01897549, 0.1054884 ], [ 0.13664603, 0.32101518, 0.01623404, -0.16614792], [0.13971895, 0.18623725, 0.01824667, 0.12197756], [ 0.14498287, 0.31902549, 0.01577184, -0.14998984], [0.14802329, 0.18426792, 0.01804764, 0.13792728], [ 0.15325488, 0.31706652, 0.01583431, -0.13414095], [0.15626298, 0.18230827, 0.01837181, 0.15378751], [ 0.16146204, 0.31509495, 0.01642226, -0.11815472], [0.16443723, 0.18031493, 0.01922733, 0.17000465], [ 0.16960284, 0.31306705, 0.01755124, -0.10158141], [0.17362317, 0.2436564 , 0.01837518, 0.04993599], [ 0.17983352, 0.37638485, 0.01471359, -0.22191451], [0.18382005, 0.24160755, 0.01579659, 0.06563613], [ 0.18999843, 0.37444776, 0.01237538, -0.20734592], [0.19395459, 0.23976643, 0.01368236, 0.07921054], [ 0.2001041 , 0.37269816, 0.01046984, -0.19469801], [0.20403267, 0.23809554, 0.01197209, 0.09104566], [ 0.21015584, 0.3711012 , 0.0089425 , -0.18361187], [0.21405911, 0.2365621 , 0.01061689, 0.10147822], [ 0.22015795, 0.36962643, 0.00774963, -0.17377296], [0.22403772, 0.23513731, 0.00957791, 0.1108048 ], [ 0.23011379, 0.36824679, 0.00685703, -0.16490224], [0.23397142, 0.23379542, 0.00882532, 0.11929026], [ 0.24002589, 0.36693785, 0.00623897, -0.15674834], [0.24386236, 0.23251303, 0.00833736, 0.12717521], [ 0.24989603, 0.36567718, 0.00587753, -0.14908046], [0.25371197, 0.23126842, 0.0080998 , 0.13468289], [ 0.25972527, 0.36444375, 0.00576205, -0.14168182], [ 0.26468942, 0.30085573, 0.00565615, -0.00641811], [ 0.27185148, 0.43406442, 0.00097498, -0.28370717], [ 0.2767978 , 0.29977748, 0.0009441 , -0.0018714 ], [ 0.28394532, 0.43318379, -0.00369537, -0.2811802 ], [ 0.28888032, 0.29909033, -0.00371737, -0.00133335], [ 0.2960197 , 0.43269032, -0.00838072, -0.28262758], [ 0.30094978, 0.29879192, -0.00845961, -0.00478087], [ 0.30808747, 0.43258706, -0.01321296, -0.28808194], [ 0.31301916, 0.29889086, -0.01341596, -0.01230318], [ 0.32016182, 0.43288791, -0.01832781, -0.29768789], [ 0.32510203, 0.2994068 , -0.01872553, -0.02410391], [ 0.3322567 , 0.43361777, -0.02386867, -0.3117055 ], [ 0.33721283, 0.30037084, -0.02453703, -0.0405065 ], [ 0.34438726, 0.43481326, -0.02999056, -0.33051702], [ 0.34936738, 0.30182642, -0.03101294, -0.06196272], [ 0.35657004, 0.43652332, -0.03686444, -0.35463649], [ 0.36158323, 0.30383024, -0.03833401, -0.08906451], [ 0.36882359, 0.43881047, -0.04468194, -0.38472289], [ 0.37388009, 0.30645362, -0.04670417, -0.12255946], [ 0.38116899, 0.44175202, -0.0536605 , -0.4215962 ], [ 0.38628042, 0.30978391, -0.05635611, -0.16336989], [ 0.39363021, 0.44544125, -0.06404935, -0.46625718], [ 0.39880997, 0.3139261 , -0.06755751, -0.21261568], [ 0.40408269, 0.31955814, -0.07163439, -0.24708359], [ 0.40719378, 0.1885509 , -0.07158771, 0.00282895], [ 0.41255444, 0.32488781, -0.07664111, -0.30626675], [ 0.41575706, 0.19409806, -0.07760724, -0.05855293], [ 0.4212127 , 0.3306455 , -0.08371154, -0.36995801], [ 0.42451543, 0.20016602, -0.08578032, -0.12538053], [ 0.43007582, 0.33699384, -0.09303868, -0.43990037], [ 0.43349013, 0.20692663, -0.09633033, -0.19949389], [ 0.43916789, 0.34410629, -0.10487711, -0.51798725], [ 0.44270819, 0.21456511, -0.10954522, -0.28291559], [ 0.44411758, 0.08541618, -0.11037625, -0.05036534], [ 0.44779995, 0.22317375, -0.11655231, -0.37430653], [ 0.44935644, 0.09433349, -0.11894317, -0.1449008 ], [ 0.45319033, 0.23235637, -0.12673023, -0.47194275], [ 0.45490581, 0.10396892, -0.13080828, -0.24715507], [ 0.4589045 , 0.24234475, -0.14035244, -0.57843369], [ 0.46079484, 0.11456552, -0.14629045, -0.35987899], [ 0.4605844 , -0.01275353, -0.14867483, -0.1445086 ], [ 0.46266755, 0.12625238, -0.15663263, -0.48229083], [ 0.46265751, -0.00060827, -0.16111498, -0.27165699], [ 0.46494651, 0.13872597, -0.17124017, -0.61364764], [ 0.46515298, 0.0125146 , -0.17800122, -0.40976131], [ 0.4632853 , -0.11319312, -0.18145764, -0.20947994], [ 0.46372721, 0.0267815 , -0.19067203, -0.55844694], [ 0.46210414, -0.098368 , -0.19667836, -0.36402026], [ 0. , 0. , -0.09041895, 0. ], [ 0.00226296, 0.13714892, -0.09563982, -0.31641611], [ 0.00238525, 0.0074115 , -0.09691716, -0.07741496], [ 0.00093836, -0.08769027, -0.0954512 , 0.08884611], [ 0.00175828, 0.04969226, -0.09923892, -0.22955902], [ 0.00044159, -0.07979959, -0.09911226, 0.00767639], [ 0.00139368, 0.05770243, -0.10426134, -0.31206498], [ 0.00021277, -0.07157005, -0.10553285, -0.07706144], [ 0.00130407, 0.06613928, -0.11211906, -0.39916435], [ 0.00026812, -0.06278472, -0.11488608, -0.16769835], [-0.00289087, -0.19145377, -0.11385934, 0.06222698], [-0.00377151, -0.05337216, -0.11820047, -0.26309913], [-0.00677287, -0.18190071, -0.11877168, -0.03461828], [-0.0074934 , -0.04366829, -0.12473954, -0.36168826], [-0.01032988, -0.17190844, -0.12698591, -0.13614406], [-0.01403516, -0.22456241, -0.12811793, -0.06860775], [-0.0154541 , -0.08599637, -0.13470265, -0.3990739 ], [-0.01898073, -0.21373513, -0.13764209, -0.17814709], [-0.0246103 , -0.34118563, -0.13696352, 0.04112572], [-0.02794707, -0.20222871, -0.14179204, -0.29263839], [-0.03338389, -0.32950401, -0.1430326 , -0.07518487], [-0.03652511, -0.19037716, -0.14981429, -0.41101179], [-0.04176055, -0.31729949, -0.15306787, -0.19718707], [-0.04764485, -0.35662404, -0.15580773, -0.16605163], [-0.05122701, -0.21710102, -0.16416128, -0.50627613], [-0.05689193, -0.34332842, -0.16909663, -0.29911253], [-0.06463252, -0.46912652, -0.17065814, -0.09463681], [-0.07006187, -0.32905132, -0.17791913, -0.44005966], [-0.07756057, -0.45446667, -0.18187229, -0.23958592], [-0.08375417, -0.37537032, -0.18950017, -0.46229526], [-0.09200697, -0.50016886, -0.19391149, -0.2673524 ], [-0.10037873, -0.50737953, -0.19912463, -0.31594881], [0. , 0. , 0.04505882, 0. ], [ 0.00217417, 0.13176766, 0.04076737, -0.26008806], [ 0.00211166, -0.00378851, 0.0413977 , 0.03820178], [ 0.00422603, 0.12814368, 0.03770934, -0.22353643], [ 0.00410568, -0.00729355, 0.03892176, 0.07347947], [ 0.00616407, 0.12475042, 0.03579699, -0.18937965], [ 0.00598898, -0.01061123, 0.03755966, 0.1068285 ], [ 0.00799365, 0.1214954 , 0.03497481, -0.15665749], [ 0.00776542, -0.0138325 , 0.03727143, 0.13918892], [ 0.0097172 , 0.11828978, 0.03521801, -0.12444987], [ 0.00943595, -0.01704548, 0.03804729, 0.17147136], [ 0.01133421, 0.11504601, 0.03653174, -0.09185159], [ 0.01099863, -0.02033786, 0.03990734, 0.2045822 ], [ 0.01284128, 0.11167557, 0.03895121, -0.0579474 ], [ 0.0168612 , 0.24363142, 0.03365967, -0.32069919], [ 0.01864733, 0.10825045, 0.03324325, -0.02523753], [ 0.0226149 , 0.24045868, 0.0284495 , -0.29053038], [0.02435206, 0.10528214, 0.0284951 , 0.00276367], [ 0.02827409, 0.23769899, 0.02412864, -0.26463386], [0.02996854, 0.10269391, 0.0245717 , 0.02685199], [ 0.0338507 , 0.23528227, 0.0205741 , -0.24227893], [0.03550762, 0.10041963, 0.02136135, 0.04771194], [ 0.03935456, 0.23314779, 0.01768458, -0.2228341 ], [0.04097819, 0.09840186, 0.01877253, 0.06593609], [ 0.04479369, 0.23124246, 0.01537766, -0.20574944], [0.04638743, 0.09659041, 0.01673136, 0.08204206], [ 0.0501745 , 0.22951955, 0.01358742, -0.19054155], [0.05174103, 0.09494086, 0.01517946, 0.09648709], [ 0.05550199, 0.22793737, 0.01226259, -0.17678012], [0.05704331, 0.09341343, 0.01407232, 0.10968085], [ 0.06077987, 0.22645819, 0.01136507, -0.16407588], [0.06229741, 0.09197177, 0.01337802, 0.12199712], [ 0.06601068, 0.22504719, 0.01086887, -0.15206976], [0.06750529, 0.09058204, 0.01307631, 0.13378422], [ 0.07119587, 0.22367141, 0.01075933, -0.14042279], [0.07266787, 0.0892119 , 0.01315802, 0.145375 ], [ 0.0763358 , 0.22229898, 0.01103271, -0.12880653], [0.07778499, 0.0878296 , 0.0136248 , 0.15709604], [ 0.08142981, 0.22089814, 0.01169605, -0.11689391], [0.08285546, 0.08640312, 0.01448912, 0.16927701], [ 0.08647615, 0.2194363 , 0.01276734, -0.10434998], [0.087877 , 0.08489928, 0.01577463, 0.18225977], [ 0.091472 , 0.21787928, 0.01427606, -0.09082256], [0.09284617, 0.08328279, 0.01751679, 0.19640802], [ 0.09641331, 0.21619022, 0.01626391, -0.07593231], [0.09935115, 0.17805134, 0.01642533, 0.00978301], [ 0.10448141, 0.31092498, 0.01208784, -0.26287788], [0.10739079, 0.17632583, 0.01247726, 0.02360123], [ 0.11249537, 0.30936846, 0.00833943, -0.25077781], [0.11538159, 0.17492233, 0.00890231, 0.03411394], [ 0.12046552, 0.308117 , 0.00491238, -0.2418139 ], [0.12333341, 0.17381153, 0.00559916, 0.04162318], [ 0.12840132, 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with tf.Graph().as_default(), tf.Session() as sess:
policy = Policy(sess)
sess.run(tf.global_variables_initializer())
start_at = datetime.datetime.now()
print("beginning training...")
policy.train_nets(batch_observations, batch_actions, batch_advantages)
done_at = datetime.datetime.now()
delta = (done_at - start_at).seconds
print("done with training after {} seconds".format(delta))
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