diff --git a/rollout.py b/rollout.py
index 2176e5328e5508476b9c0df4ab8e4d10b5f61ec3..a9fc5c1bb7b8eae7795724a6920f05663718d1e3 100644
--- a/rollout.py
+++ b/rollout.py
@@ -1,17 +1,22 @@
 #!/usr/bin/env python
 
-############################################################
-## Ideally you shouldn't need to change this file at all  ##
-############################################################
-
+################################################################
+## Ideally you shouldn't need to change this file at all      ##
+##                                                            ##
+## This file generates the rollouts, with the specific agent, ##
+## batch_size and wrappers specified in subminssion_config.py ##
+################################################################
+from tqdm import tqdm
 import numpy as np
 
 from envs.batched_env import BactchedEnv
 from submission_config import SubmissionConfig
 
+NUM_ASSESSMENTS = 512
+
 def run_batched_rollout(batched_env, agent):
     """
-    This function will be called the rollout
+    This function will generate a series of rollouts in a batched manner.
     """
 
     num_envs = batched_env.num_envs
@@ -22,26 +27,36 @@ def run_batched_rollout(batched_env, agent):
     dones = [False for _ in range(num_envs)]
     infos = [{} for _ in range(num_envs)]
 
+    # We assign each environment a fixed number of episodes at the start
+    envs_each = NUM_ASSESSMENTS // num_envs
+    remainders = NUM_ASSESSMENTS % num_envs
+    episodes = [envs_each + int(i < remainders) for i in range(num_envs)]
+    
     episode_count = 0
+    pbar = tqdm(total=NUM_ASSESSMENTS)
 
     # The evaluator will automatically stop after the episodes based on the development/test phase
-    while episode_count < 10000:
+    while episode_count < NUM_ASSESSMENTS:
         actions = agent.batched_step(observations, rewards, dones, infos)
 
         observations, rewards, dones, infos = batched_env.batch_step(actions)
         for done_idx in np.where(dones)[0]:
             observations[done_idx] = batched_env.single_env_reset(done_idx)
-            episode_count += 1
-            print("Episodes Completed :", episode_count)
+            
+            if episodes[done_idx] > 0:
+                episodes[done_idx] -= 1
+                episode_count += 1
+                pbar.update(1)
 
-if __name__ == "__main__":
 
+if __name__ == "__main__":
     submission_env_make_fn = SubmissionConfig.submission_env_make_fn
     NUM_PARALLEL_ENVIRONMENTS = SubmissionConfig.NUM_PARALLEL_ENVIRONMENTS
     Agent = SubmissionConfig.Submision_Agent
 
-    batched_env = BactchedEnv(env_make_fn=submission_env_make_fn,
-                              num_envs=NUM_PARALLEL_ENVIRONMENTS)
+    batched_env = BactchedEnv(
+        env_make_fn=submission_env_make_fn, num_envs=NUM_PARALLEL_ENVIRONMENTS
+    )
 
     num_envs = batched_env.num_envs
     num_actions = batched_env.num_actions
@@ -49,4 +64,3 @@ if __name__ == "__main__":
     agent = Agent(num_envs, num_actions)
 
     run_batched_rollout(batched_env, agent)
-
diff --git a/submission_config.py b/submission_config.py
index f7d548260474eb7a780ed98f8b57c7a3e28c6f23..5038d32ce0c8ee1498a5ff30ad373e9f13f1ab3c 100644
--- a/submission_config.py
+++ b/submission_config.py
@@ -23,7 +23,7 @@ class SubmissionConfig:
     ## Change the NUM_PARALLEL_ENVIRONMENTS as you need
     ## for example reduce it if your GPU doesn't fit
     ## Increasing above 32 is not advisable for the Nethack Challenge 2021
-    NUM_PARALLEL_ENVIRONMENTS = 16
+    NUM_PARALLEL_ENVIRONMENTS = 32
 
 
     ## Add a function that creates your nethack env
diff --git a/submission_wrappers.py b/submission_wrappers.py
index c35fa17e5eb82a90df80f5f01aaff2c6112c9a28..9f75d9de6baad97a888c7d462f6a7e6af6575625 100644
--- a/submission_wrappers.py
+++ b/submission_wrappers.py
@@ -8,5 +8,5 @@ def addtimelimitwrapper_fn():
     Should return a gym env which wraps the nethack gym env
     """
     env = nethack_make_fn()
-    env = TimeLimit(env, max_episode_steps=10_000_0000)
+    env = TimeLimit(env, max_episode_steps=10_000_000)
     return env
\ No newline at end of file