diff --git a/examples/manual_play/learn_model.py b/examples/manual_play/learn_model.py
index 0743fb2a2..952cc5b52 100644
--- a/examples/manual_play/learn_model.py
+++ b/examples/manual_play/learn_model.py
@@ -38,30 +38,30 @@
             o, r, done, _, info = csle_cyborg_env.step(action=a)
             s_prime = info[constants.ENV_METRICS.STATE]
             oid = info[constants.ENV_METRICS.OBSERVATION]
-            if (s, s_prime, a) not in transition_probabilities:
-                transition_probabilities[(s, s_prime, a)] = 1
+            if ",".join([str(s), str(s_prime), str(a)]) not in transition_probabilities:
+                transition_probabilities[",".join([str(s), str(s_prime), str(a)])] = 1
                 new_transitions += 1
             else:
-                transition_probabilities[(s, s_prime, a)] = transition_probabilities[(s, s_prime, a)] + 1
-            if (s, s_prime, a) not in reward_function:
-                reward_function[(s, s_prime, a)] = r
-            if (s_prime, oid) not in observation_probabilities:
-                observation_probabilities[(s_prime, oid)] = 1
+                transition_probabilities[",".join([str(s), str(s_prime), str(a)])] = transition_probabilities[",".join([str(s), str(s_prime), str(a)])] + 1
+            if ",".join([str(s), str(s_prime), str(a)]) not in reward_function:
+                reward_function[",".join([str(s), str(s_prime), str(a)])] = r
+            if ",".join([str(s_prime), str(oid)]) not in observation_probabilities:
+                observation_probabilities[",".join([str(s_prime), str(oid)])] = 1
             else:
-                observation_probabilities[(s_prime, oid)] = observation_probabilities[(s_prime, oid)] + 1
+                observation_probabilities[",".join([str(s_prime), str(oid)])] = observation_probabilities[",".join([str(s_prime), str(oid)])] + 1
             t_count += 1
         print(f"new transitions: {new_transitions}")
 
-        # if i % save_every == 0:
-        #     model = {}
-        #     model["transitions"] = transition_probabilities
-        #     model["rewards"] = reward_function
-        #     model["episodes"] = i
-        #     model["steps"] = t_count
-        #     model["observations"] = observation_probabilities
-        #     model["initial_state"] = initial_state_distribution
-        #     json_str = json.dumps(model, indent=4, sort_keys=True)
-        #     with io.open(f"/home/kim/cyborg_model_{i}.json", 'w', encoding='utf-8') as f:
-        #         f.write(json_str)
+        if i % save_every == 0:
+            model = {}
+            model["transitions"] = transition_probabilities
+            model["rewards"] = reward_function
+            model["episodes"] = i
+            model["steps"] = t_count
+            model["observations"] = observation_probabilities
+            model["initial_state"] = initial_state_distribution
+            json_str = json.dumps(model, indent=4, sort_keys=True)
+            with io.open(f"/home/kim/cyborg_model_{i}.json", 'w', encoding='utf-8') as f:
+                f.write(json_str)