diff --git a/config.yaml b/config.yaml
index 03e9fe3..a9a2b69 100644
--- a/config.yaml
+++ b/config.yaml
@@ -5,7 +5,7 @@ wandb:
 
 debug:
   env:
-    headless: False
+    headless: True
     stream_wrapper: False
     init_state: cut
   train:
@@ -23,7 +23,7 @@ debug:
     checkpoint_interval: 4
     save_overlay: True
     overlay_interval: 4
-    verbose: True
+    verbose: True 
     env_pool: False
     log_frequency: 5000
 
diff --git a/pokemonred_puffer/cleanrl_puffer.py b/pokemonred_puffer/cleanrl_puffer.py
index 17acbb7..dfd98bc 100644
--- a/pokemonred_puffer/cleanrl_puffer.py
+++ b/pokemonred_puffer/cleanrl_puffer.py
@@ -276,7 +276,7 @@ def __init__(
                 torch.zeros(shape, device=self.device),
                 torch.zeros(shape, device=self.device),
             )
-        self.obs = torch.zeros(config.batch_size + 1, *obs_shape)
+        self.obs = torch.zeros(config.batch_size + 1, *obs_shape, dtype=torch.uint8)
         self.actions = torch.zeros(config.batch_size + 1, *atn_shape, dtype=int)
         self.logprobs = torch.zeros(config.batch_size + 1)
         self.rewards = torch.zeros(config.batch_size + 1)
@@ -284,7 +284,7 @@ def __init__(
         self.truncateds = torch.zeros(config.batch_size + 1)
         self.values = torch.zeros(config.batch_size + 1)
 
-        self.obs_ary = np.asarray(self.obs)
+        self.obs_ary = np.asarray(self.obs, dtype=np.uint8)
         self.actions_ary = np.asarray(self.actions)
         self.logprobs_ary = np.asarray(self.logprobs)
         self.rewards_ary = np.asarray(self.rewards)
@@ -534,11 +534,11 @@ def train(self):
                 )
 
         # Flatten the batch
-        self.b_obs = b_obs = torch.Tensor(self.obs_ary[b_idxs])
-        b_actions = torch.Tensor(self.actions_ary[b_idxs]).to(self.device, non_blocking=True)
-        b_logprobs = torch.Tensor(self.logprobs_ary[b_idxs]).to(self.device, non_blocking=True)
+        self.b_obs = b_obs = torch.tensor(self.obs_ary[b_idxs], dtype=torch.uint8)
+        b_actions = torch.tensor(self.actions_ary[b_idxs]).to(self.device, non_blocking=True)
+        b_logprobs = torch.tensor(self.logprobs_ary[b_idxs]).to(self.device, non_blocking=True)
         # b_dones = torch.Tensor(self.dones_ary[b_idxs]).to(self.device, non_blocking=True)
-        b_values = torch.Tensor(self.values_ary[b_idxs]).to(self.device, non_blocking=True)
+        b_values = torch.tensor(self.values_ary[b_idxs]).to(self.device, non_blocking=True)
         b_advantages = advantages.reshape(
             config.batch_rows, num_minibatches, config.bptt_horizon
         ).transpose(0, 1)
@@ -547,7 +547,9 @@ def train(self):
         # Optimizing the policy and value network
         train_time = time.time()
         pg_losses, entropy_losses, v_losses, clipfracs, old_kls, kls = [], [], [], [], [], []
-        mb_obs_buffer = torch.zeros_like(b_obs[0], pin_memory=(self.device == "cuda"))
+        mb_obs_buffer = torch.zeros_like(
+            b_obs[0], pin_memory=(self.device == "cuda"), dtype=torch.uint8
+        )
 
         for epoch in range(config.update_epochs):
             lstm_state = None
diff --git a/pokemonred_puffer/policies/multi_convolutional.py b/pokemonred_puffer/policies/multi_convolutional.py
index f3cc06b..ced205e 100644
--- a/pokemonred_puffer/policies/multi_convolutional.py
+++ b/pokemonred_puffer/policies/multi_convolutional.py
@@ -82,30 +82,19 @@ def encode_observations(self, observations):
             screen = torch.index_select(
                 self.screen_buckets,
                 0,
-                screen.flatten()
-                .unsqueeze(-1)
-                .bitwise_and(self.unpack_mask)
-                .bitwise_right_shift(self.unpack_shift)
-                .flatten()
-                .int(),
+                ((screen.reshape((-1, 1)) & self.unpack_mask) >> self.unpack_shift).flatten().int(),
             ).reshape(restored_shape)
             visited_mask = torch.index_select(
                 self.linear_buckets,
                 0,
-                visited_mask.flatten()
-                .unsqueeze(-1)
-                .bitwise_and(self.unpack_mask)
-                .bitwise_right_shift(self.unpack_shift)
+                ((visited_mask.reshape((-1, 1)) & self.unpack_mask) >> self.unpack_shift)
                 .flatten()
                 .int(),
             ).reshape(restored_shape)
             global_map = torch.index_select(
                 self.linear_buckets,
                 0,
-                global_map.flatten()
-                .unsqueeze(-1)
-                .bitwise_and(self.unpack_mask)
-                .bitwise_right_shift(self.unpack_shift)
+                ((global_map.reshape((-1, 1)) & self.unpack_mask) >> self.unpack_shift)
                 .flatten()
                 .int(),
             ).reshape(restored_shape)