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Traceback (most recent call last):
File "./sc2agents/sc2agents/train_a2c.py", line 53, in
main()
File "./sc2agents/sc2agents/train_a2c.py", line 49, in main
policy='cnn', lrschedule='constant', num_cpu=4)
File "./sc2agents/sc2agents/train_a2c.py", line 39, in train
learn(policy_fn, env, seed, total_timesteps=int(num_timesteps * 1.1), lrschedule=lrschedule)
File "/usr/local/lib/python3.6/dist-packages/baselines/a2c/a2c.py", line 156, in learn
max_grad_norm=max_grad_norm, lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps, lrschedule=lrschedule)
File "/usr/local/lib/python3.6/dist-packages/baselines/a2c/a2c.py", line 35, in init
step_model = policy(sess, ob_space, ac_space, nenvs, 1, reuse=False)
File "/usr/local/lib/python3.6/dist-packages/baselines/a2c/policies.py", line 108, in init
h = nature_cnn(X)
File "/usr/local/lib/python3.6/dist-packages/baselines/a2c/policies.py", line 12, in nature_cnn
h = activ(conv(scaled_images, 'c1', nf=32, rf=8, stride=4, init_scale=np.sqrt(2)))
File "/usr/local/lib/python3.6/dist-packages/baselines/a2c/utils.py", line 59, in conv
return b + tf.nn.conv2d(x, w, strides=strides, padding=pad, data_format=data_format)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 956, in conv2d
data_format=data_format, dilations=dilations, name=name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 3392, in create_op
op_def=op_def)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 1734, in init
control_input_ops)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 1570, in _create_c_op
raise ValueError(str(e))
ValueError: Negative dimension size caused by subtracting 8 from 1 for 'model/c1/Conv2D' (op: 'Conv2D') with input shapes: [4,1,64,64], [8,8,64,32].
The text was updated successfully, but these errors were encountered:
Issue confirmed. As far as I can tell, this is likely due to the baselines code getting updated.
The train_dqn.py file still runs as far as I could tell. I have been planning on replacing the baselines-based implementation with a self-contained one for a while now, so that's what I will do instead of trying to hunt down the issue. I believe that will provide more of an educational value to anyone reading the code as well.
For a similar standalone and fully-functional implementation of A3C for SC2 minigames, this repo provides a good example. I have managed to run it and reproduce their results.
I ran
python train_a2c.py
but ran into an error. The trace is provided below.I also have a Google Colab notebook showing this error here (it is using Tensorflow 1.4): https://colab.research.google.com/drive/1vQc0vbO0waUa2QCmMLqcPVhpJ4ElOtRR
The text was updated successfully, but these errors were encountered: