NVlabs / GA3C

Hybrid CPU/GPU implementation of the A3C algorithm for deep reinforcement learning.
BSD 3-Clause "New" or "Revised" License
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TRAINING_MIN_BATCH_SIZE does not seem to affect anything #6

Closed etienne87 closed 7 years ago

etienne87 commented 7 years ago

In ThreadTrainer.py, I don't understand how the following lines are supposed to affect the batch size :

np.concatenate((x__, x_))
np.concatenate((r__, r_))
np.concatenate((a__, a_))

np.concatenate returns the merged array, but does not affect x_ or x.

However, I do measure the TPS to drops. What sorcery is this ?

[Time: 404] [Episode: 213 Score: -1.0642] [RScore: 7.5345 RPPS: 281] [PPS: 282 TPS: 4] [NT: 2 NP: 3 NA: 4]

(The PPS/ TPS is overall low in my case because the game is a costly one running on remote desktop)

EDIT : i suggest to modify to :

x__ = np.concatenate((x__, x_))
r__ = np.concatenate((r__, r_))
a__ = np.concatenate((a__, a_))

but this does not affect TPS compared to other

mbz commented 7 years ago

yeap, that's a bug right there! thanks for noticing it. that's what happens when we don't test all the configurations. please submit a pull request if you can. otherwise I will go ahead and fix it.

etienne87 commented 7 years ago

okey dokey