Tensorflow: Different results with the same random seed

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I'm running a reinforcement learning program in a gym environment(BipedalWalker-v2) implemented in tensorflow. I've set the random seed of the environment, tensorflow and numpy manually as follows

os.environ['PYTHONHASHSEED']=str(42)
random.seed(42)
np.random.seed(42)
tf.set_random_seed(42)

env = gym.make('BipedalWalker-v2')
env.seed(0)

config = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# run the graph with sess

However, I get different results every time I run my program (without changing any code). Why are the results not consistent and what should I do if I want to obtain the same result?

Update:

The only places that I can think of may introduce randomness (other than the neural networks) are

  1. I use tf.truncated_normal to generate random noise epsilon so as to implement noisy layer
  2. I use np.random.uniform to randomly select samples from replay buffer

I also spot that the scores I get are pretty consistent at the first 10 episodes, but then begin to differ. Other things such as losses also show a similar trend but are not the same in numeric.

Update 2

I've also set "PYTHONHASHSEED" and use single-thread CPU as @jaypops96 described, but still cannot reproduce the result. Code has been updated in the above code block

openai-gym tensorflow random-seed random python
3 Answers

I suggest checking whether your TensorFlow graph contains nondeterministic operations. For example, reduce_sum before TensorFlow 1.2 was one such operation. These operations are nondeterministic because floating-point addition and multiplication are nonassociative (the order in which floating-point numbers are added or multiplied affects the result) and because such operations don't guarantee their inputs are added or multiplied in the same order every time. See also this question.

EDIT (Sep. 20, 2020): The GitHub repository framework-determinism has more information about sources of nondeterminism in machine learning frameworks, particularly TensorFlow.

user_8157240

It seems that tensorflow neural networks introduce randomness during training that isn't controlled by a numpy random seed. The randomness appears to possibly come from python hash operations and parallelized operations executing in non-controlled ordering, at the very least.

I had success getting 100% reproducibility using a keras-tensorflow NN, by following the setup steps in this response: How to get reproducible results in keras

specifically, I used the formulation proposed by @Poete Maudit in that link.

They key was to set random seed values UP FRONT, for numpy, python, and tensorflow, then also to make tensorflow run on single-thread CPU in a specially-configured session.

Here's the code i used, updated very slightly from the link i posted. 

print('Running in 1-thread CPU mode for fully reproducible results training a CNN and generating numpy randomness.  This mode may be slow...')
# Seed value
# Apparently you may use different seed values at each stage
seed_value= 1

# 1. Set `PYTHONHASHSEED` environment variable at a fixed value
import os
os.environ['PYTHONHASHSEED']=str(seed_value)
seed_value += 1

# 2. Set `python` built-in pseudo-random generator at a fixed value
import random
random.seed(seed_value)
seed_value += 1

# 3. Set `numpy` pseudo-random generator at a fixed value
import numpy as np
np.random.seed(seed_value)
seed_value += 1

# 4. Set `tensorflow` pseudo-random generator at a fixed value
import tensorflow as tf
tf.set_random_seed(seed_value)

# 5. Configure a new global `tensorflow` session
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)
tf.keras.backend.set_session(sess)

#rest of code...
user_108698040

Maybe you can try to set the number of parallelism threads to 1. I have the same problem: the loss became different to the seventh decimal place start from the second episode. It fixed when I set 

tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
user_81989810
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