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?
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