Unable to visualize a trained RL2-PPO model

[darshitajain]

I was referring to this document - https://garage.readthedocs.io/en/latest/user/reuse_garage_policy.html to visualize the RL2-PPO model, trained on Metaworld's ML1 Push-v2 environment for 700 epochs.

Below is the code used for visualizing the trained policy-

`# Load the policy and the env in which it was trained from garage.experiment import Snapshotter import tensorflow as tf # optional, only for TensorFlow as we need a tf.Session import metaworld import random

To check if it worked with the environment on which it was trained

ml1 = metaworld.ML1('push-v2') # Construct the benchmark, sampling tasks

env = ml1.train_classes['push-v2']() # Create an environment with task pick_place

task = random.choice(ml1.train_tasks)

env.set_task(task) # Set task

snapshotter = Snapshotter() with tf.compat.v1.Session(): # optional, only for TensorFlow data = snapshotter.load('data/local/experiment/rl2_ppo_metaworld_ml1_push_resume_training_3') policy = data['algo'].policy env = data['env'] print('env', env) print("Data dictionary",data)

from garage import rollout path = rollout(env, policy, animated=True) print(path) `

This was the output I got-

I used this script https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/rl2_ppo_metaworld_ml1_push.py for training. This is the TensorBoard visualization after training for 700 epochs. https://tensorboard.dev/experiment/uvK1lPVSQmWDDJQerP0bEQ/#scalars

Request your help @haydenshively @krzentner

Thank you.

[krzentner]

You'll probably want to call rollout(env[0], polichy, animated=True), since env is actually a list of environments in this case.

[ruffiann]

hi darshitajain! I would like to know if you have solved this problem, as I am also experiencing this problem. I would be grateful if you could help!

[darshitajain]

Hi @ruffiann. I was unable to resolve that. Due to time constraints, I switched to a different RL environment.