[FIXED] Printing model summaries for rllib models

Issue

I have not seen anything in the rllib documentation that would allow me to print a quick summary of the model like print(model.summary()) in keras. I tried using tf-slim and

variables = tf.compat.v1.model_variables()
slim.model_analyzer.analyze_vars(variables, print_info=True)

to get a rough idea for tensorflow models, but this found no variables after the model was initialized (inserted at the end of the ESTrainer class _init). Specifically, I have been trying to get a summary of an Evolutionary Strategy (ES) policy to verify that the changes to the model config are being updated as expected, but I have not been able to get a summary print working.

Is there an existing method for this? Is slim expected to work here?

Solution

The training agent can return the policy which gives you access to the model:

agent = ppo.PPOTrainer(config, env=select_env)

policy = agent.get_policy()
policy.model.base_model.summary() # Prints the model summary

Sample output:

 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 observations (InputLayer)      [(None, 7)]          0           []                               
                                                                                                  
 fc_1 (Dense)                   (None, 256)          2048        ['observations[0][0]']           
                                                                                                  
 fc_value_1 (Dense)             (None, 256)          2048        ['observations[0][0]']           
                                                                                                  
 fc_2 (Dense)                   (None, 256)          65792       ['fc_1[0][0]']                   
                                                                                                  
 fc_value_2 (Dense)             (None, 256)          65792       ['fc_value_1[0][0]']             
                                                                                                  
 fc_out (Dense)                 (None, 5)            1285        ['fc_2[0][0]']                   
                                                                                                  
 value_out (Dense)              (None, 1)            257         ['fc_value_2[0][0]']             
                                                                                                  
==================================================================================================
Total params: 137,222
Trainable params: 137,222
Non-trainable params: 0

Answered By - Francois Beaussier