How to achieve replicable results in SenseAct? about senseact HOT 3 OPEN

Hi @fisherxue, we had a similar discussion in this issue. Please look at the entire discussion and try the steps listed there. If you're still running into issues, let me know.

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What I've done: saved the random state in a file, then loaded it using pickle. I do this before the env is loaded. I then set the tensorflow random seed and the python random seed after sess.enter()

I am also passing the random state I load from file into the environment.
I'm fairly sure my hardware is not the bottleneck.

I am able to get relatively consistent results when I run two simulations at the same time. However, when I run one after the other, I get vastly different results. Any advice?

This is what I have:

# use fixed random state
with open('random.obj', 'rb') as f:
    rand_state = pickle.load(f)
np.random.set_state(rand_state)
tf_set_seeds(np.random.randint(1, 2**31 - 1))

#Create Asynchronous Simulation of InvertedDoublePendulum-v2 mujoco environment.
env = DoubleInvertedPendulumEnv(agent_dt=0.005,
                                sensor_dt=[0.01, 0.0033333],
                                is_render=False,
                                random_state=rand_state
                               )
# Start environment processes
env.start()

# Create baselines ppo policy function
sess = U.single_threaded_session()
sess.__enter__()
seed = np.random.randint(1, 2**31 - 1)
tf.set_random_seed(seed)
random.seed(seed)

Thanks!

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@gauthamvasan
I'm getting this warning when running on one machine:
WARNING:root:Agent has over-run its allocated dt, it has been 0.008300065994262695 since the last observation, 0.003300065994262695 more than allowed
However, on the other machine I'm running it on, I only get that warning at the start of each iteration.
I'm still failing to get tight repeatability curves on double pendulum.

Any tips?
Thanks!

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