import colored_traceback.always
from correct_overapprox import *
import tensorflow as tf 
import numpy as np
import os
import joblib
# script to create concatted model

policy_file = "/Users/Chelsea/" # mac
policy_file = policy_file + "Dropbox/AAHAA/src/rllab/data/local/experiment/"
################################################################################
policy_name = "EXCELLENT_POLICY_relu_small_network_ppo_capped_action_simpler_dense_layer_xW_learn_std_smaller_learning_rate/"

################################################################################
policy_file = policy_file + policy_name + "params.pkl"

verbose = True
output_pb = True
output_nnet = False
f_id = str(int(np.round(np.random.rand()*5000)))
tensorboard_log_dir = "/Users/Chelsea/Dropbox/AAHAA/src/OverApprox/tensorboard_logs/real_controller_correct_accel_bound_"+f_id
network_dir = "/Users/Chelsea/Dropbox/AAHAA/src/OverApprox/nnet_files"
nsteps = 5


build_model(nsteps, 
                output_pb, 
                output_nnet, 
                tensorboard_log_dir, 
                network_dir, 
                f_id,
                policy_file=policy_file, 
                activation_type="Relu",
                activation_fn=tf.nn.relu,
                dynamics_fun="constant",
                m=0.25,
                l=0.1,
                verbose=verbose)