diff --git a/README.txt b/README.txt
index 9382cb129c545de63e5b98df07e141bdc5427596..acc4887474a3c98e659fccf7616cac4a9f0cac05 100644
--- a/README.txt
+++ b/README.txt
@@ -38,6 +38,7 @@ These are the minimum steps required to replicate the results for simple_interse
         * To test one of these trained low-level policies, for example wait: `python3 low_level_policy_main.py --option=wait --test --saved_policy_in_root`
         * Available maneuvers are: wait, changelane, stop, keeplane, follow
     * These results are visually evaluated.
+    * Note: This training has a high variance due to the continuous action space, especially for stop and keeplane maneuvers. It may help to train for 0.2 million steps than the default 0.1 million by adding argument '--nb_steps=200000' while training.
 * High-level policy:
     * To train high-level policy from scratch using the given low-level policies: `python3 high_level_policy_main.py --train`
     * To evaluate this trained high-level policy: `python3 high_level_policy_main.py --evaluate --saved_policy_in_root`.