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IntelLabs / coach

Reinforcement Learning Coach by Intel AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms
https://intellabs.github.io/coach/
Apache License 2.0
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Printing Agent Networks #307

Closed nitsanluke closed 5 years ago

nitsanluke commented 5 years ago

print_networks_summary option from the visualization params only provides the input dense layer size. I would like to know whether can get the actual input shape of the environment observation ? I'm working on my own environment and I want to verify whether my environment is correctly set up to get the correct observation shape.

galnov commented 5 years ago

PR #310 adds input size to the printed network summary.

nitsanluke commented 5 years ago

@galnov Sorry to re-open this again. I found that the prints give the same output for both LSTM and FC middleware layers. Is it something that is done purposefully.

eg:

Input Embedder: observation
    Input size = [84, 84, 4]
    Input Normalization (scale = 255.0, offset = 0.0)
    Convolution (num filters = 32, kernel size = 8, stride = 4)
    Activation (type = <function relu at 0x1318082f0>)
    Convolution (num filters = 64, kernel size = 4, stride = 2)
    Activation (type = <function relu at 0x1318082f0>)
    Convolution (num filters = 64, kernel size = 3, stride = 1)
    Activation (type = <function relu at 0x1318082f0>)
Middleware:
    Dense (num outputs = 512)
    Activation (type = <function relu at 0x1318082f0>)
Output Head: v_values_head
    Dense (num outputs = 1)
Output Head: policy_values_head
    Dense (num outputs = 6)
    Softmax

coach -r -p Atari_A3C_LSTM --print_networks_summary -lvl air_raid