diff --git a/hw2/train_pg_f18.py b/hw2/train_pg_f18.py
index ecca81964..c46440407 100644
--- a/hw2/train_pg_f18.py
+++ b/hw2/train_pg_f18.py
@@ -10,7 +10,6 @@
 import os
 import time
 import inspect
-from multiprocessing import Process
 
 #============================================================================================#
 # Utilities
@@ -81,6 +80,9 @@ def init_tf_sess(self):
         self.sess.__enter__() # equivalent to `with self.sess:`
         tf.global_variables_initializer().run() #pylint: disable=E1101
 
+    def close_tf_sess(self):
+        self.sess.__exit__(None, None, None)
+        tf.reset_default_graph()
     #========================================================================================#
     #                           ----------PROBLEM 2----------
     #========================================================================================#
@@ -629,6 +631,7 @@ def train_PG(
         logz.dump_tabular()
         logz.pickle_tf_vars()
 
+    agent.close_tf_sess()
 
 def main():
     import argparse
@@ -659,41 +662,28 @@ def main():
 
     max_path_length = args.ep_len if args.ep_len > 0 else None
 
-    processes = []
-
     for e in range(args.n_experiments):
         seed = args.seed + 10*e
         print('Running experiment with seed %d'%seed)
 
-        def train_func():
-            train_PG(
-                exp_name=args.exp_name,
-                env_name=args.env_name,
-                n_iter=args.n_iter,
-                gamma=args.discount,
-                min_timesteps_per_batch=args.batch_size,
-                max_path_length=max_path_length,
-                learning_rate=args.learning_rate,
-                reward_to_go=args.reward_to_go,
-                animate=args.render,
-                logdir=os.path.join(logdir,'%d'%seed),
-                normalize_advantages=not(args.dont_normalize_advantages),
-                nn_baseline=args.nn_baseline, 
-                seed=seed,
-                n_layers=args.n_layers,
-                size=args.size
-                )
-        # # Awkward hacky process runs, because Tensorflow does not like
-        # # repeatedly calling train_PG in the same thread.
-        p = Process(target=train_func, args=tuple())
-        p.start()
-        processes.append(p)
-        # if you comment in the line below, then the loop will block 
-        # until this process finishes
-        # p.join()
-
-    for p in processes:
-        p.join()
+        
+        train_PG(
+            exp_name=args.exp_name,
+            env_name=args.env_name,
+            n_iter=args.n_iter,
+            gamma=args.discount,
+            min_timesteps_per_batch=args.batch_size,
+            max_path_length=max_path_length,
+            learning_rate=args.learning_rate,
+            reward_to_go=args.reward_to_go,
+            animate=args.render,
+            logdir=os.path.join(logdir,'%d'%seed),
+            normalize_advantages=not(args.dont_normalize_advantages),
+            nn_baseline=args.nn_baseline, 
+            seed=seed,
+            n_layers=args.n_layers,
+            size=args.size
+            )
 
 if __name__ == "__main__":
     main()