Running frozen_graph_def.pb

So I'm trying to play around with the stored tensorflow PPO model, but I can't seem to get the discrete actions when inputting my vector observations. I see in the TFPolicy class it uses the action output named

policy_1/concat:0

however, this is not present in my frozen graph, only

policy_1/concat_2/axis:0

and

policy_1/concat_2:0

are present. I have attached my code below. What am I doing wrong? I have 2 discrete action branches with 3 possible values each.

from mlagents.tf_utils import tf



def load_pb(path_to_pb):

    with tf.gfile.GFile(path_to_pb, "rb") as f:

        graph_def = tf.GraphDef()

        graph_def.ParseFromString(f.read())

    with tf.Graph().as_default() as graph:

        tf.import_graph_def(graph_def, name='')

        return graph

GRAPH_PB_PATH = './my_path/frozen_graph_def.pb'

graph = load_pb(GRAPH_PB_PATH)

with tf.compat.v1.Session(graph=graph) as sess:

    output_node = graph.get_tensor_by_name('policy_1/concat_2:0')

    input_node = graph.get_tensor_by_name('vector_observation:0')

    action_masks = graph.get_tensor_by_name('action_masks:0')

    model_result = sess.run(

        output_node,

        feed_dict ={

        input_node : np.array(

            [[ 0.        ,  0.        , -0.29925328, -0.18826045, -0.40039593,

         0.        ,  0.        , -0.92760783,  0.13117751, -0.56681085]]),

             action_masks: np.array([[1,1,1,1,1,1]])})

model_result.round(1)

/// Results in:

array([[-15.4, -15.4,  -0. , -15.4,  -0. , -15.4]], dtype=float32)

 

Hi,
I think the tensors that you want are "

action:0

" (and possibly "

action_output_shape:0

").

For discrete actions, the values that are output are actually the log probabilities for each branch. You can see how we do it here: https://github.com/Unity-Technologi...l-agents/Runtime/Inference/ApplierImpl.cs#L44