diff --git a/dreamer4/dreamer4.py b/dreamer4/dreamer4.py
index 3881307..5504951 100644
--- a/dreamer4/dreamer4.py
+++ b/dreamer4/dreamer4.py
@@ -2058,6 +2058,8 @@ class DynamicsWorldModel(Module):
         prob_no_shortcut_train = None,              # probability of no shortcut training, defaults to 1 / num_step_sizes
         add_reward_embed_to_agent_token = False,
         add_reward_embed_dropout = 0.1,
+        add_state_pred_head = False,
+        state_pred_loss_weight = 0.1,
         num_discrete_actions: int | tuple[int, ...] = 0,
         num_continuous_actions = 0,
         continuous_norm_stats = None,
@@ -2181,6 +2183,22 @@ class DynamicsWorldModel(Module):
         self.pred_orig_latent = pred_orig_latent # x-space or v-space
         self.loss_weight_fn = loss_weight_fn
 
+        # state prediction, for state entropy bonus
+
+        self.add_state_pred_head = add_state_pred_head
+        self.state_pred_loss_weight = state_pred_loss_weight
+
+        self.should_pred_state = add_state_pred_head and state_pred_loss_weight > 0.
+
+        if self.should_pred_state:
+            self.state_pred_token = nn.Parameter(torch.randn(dim) * 1e-2)
+
+            self.to_state_pred = Sequential(
+                RMSNorm(dim),
+                nn.Linear(dim, num_latent_tokens * dim_latent * 2),
+                Rearrange('... (n d two) -> ... n d two', n = num_latent_tokens, two = 2)
+            )
+
         # reinforcement related
 
         # they sum all the actions into a single token
@@ -3387,9 +3405,13 @@ class DynamicsWorldModel(Module):
 
         agent_tokens = repeat(agent_tokens, 'b ... d -> b t ... d', t = time)
 
+        # empty token
+
+        empty_token = agent_tokens[:, :, 0:0]
+
         # maybe reward tokens
 
-        reward_tokens = agent_tokens[:, :, 0:0]
+        reward_tokens = empty_token
 
         if exists(rewards):
             two_hot_encoding = self.reward_encoder(rewards)
@@ -3429,6 +3451,13 @@ class DynamicsWorldModel(Module):
             else:
                 noised_proprio = proprio
 
+        # maybe state prediction token
+
+        if self.should_pred_state:
+            state_pred_token = repeat(self.state_pred_token, 'd -> b t 1 d', b = batch, t = time)
+        else:
+            state_pred_token = empty_token
+
         # maybe create the action tokens
 
         if exists(discrete_actions) or exists(continuous_actions):
@@ -3453,11 +3482,11 @@ class DynamicsWorldModel(Module):
             action_tokens = torch.zeros_like(agent_tokens[:, :, 0:1])
 
         else:
-            action_tokens = agent_tokens[:, :, 0:0] # else empty off agent tokens
+            action_tokens = empty_token # else empty off agent tokens
 
         # main function, needs to be defined as such for shortcut training - additional calls for consistency loss
 
-        def get_prediction(noised_latents, noised_proprio, signal_levels, step_sizes_log2, action_tokens, reward_tokens, agent_tokens, return_agent_tokens = False, return_time_cache = False):
+        def get_prediction(noised_latents, noised_proprio, signal_levels, step_sizes_log2, state_pred_token, action_tokens, reward_tokens, agent_tokens, return_agent_tokens = False, return_time_cache = False):
 
             # latents to spatial tokens
 
@@ -3506,7 +3535,7 @@ class DynamicsWorldModel(Module):
 
             # pack to tokens for attending
 
-            tokens, packed_tokens_shape = pack([flow_token, space_tokens, proprio_token, registers, action_tokens, reward_tokens, agent_tokens], 'b t * d')
+            tokens, packed_tokens_shape = pack([flow_token, space_tokens, proprio_token, state_pred_token, registers, action_tokens, reward_tokens, agent_tokens], 'b t * d')
 
             # attention
 
@@ -3514,7 +3543,7 @@ class DynamicsWorldModel(Module):
 
             # unpack
 
-            flow_token, space_tokens, proprio_token, register_tokens, action_tokens, reward_tokens, agent_tokens = unpack(tokens, packed_tokens_shape, 'b t * d')
+            flow_token, space_tokens, proprio_token, state_pred_token, register_tokens, action_tokens, reward_tokens, agent_tokens = unpack(tokens, packed_tokens_shape, 'b t * d')
 
             # pooling
 
@@ -3529,6 +3558,11 @@ class DynamicsWorldModel(Module):
 
                 pred = (pred, pred_proprio)
 
+            # maybe state pred
+
+            if self.should_pred_state:
+                pred_state = self.to_state_pred(state_pred_token)
+
             # returning
 
             if not return_agent_tokens:
@@ -3541,7 +3575,7 @@ class DynamicsWorldModel(Module):
 
         # curry into get_prediction what does not change during first call as well as the shortcut ones
 
-        _get_prediction = partial(get_prediction, action_tokens = action_tokens, reward_tokens = reward_tokens, agent_tokens = agent_tokens)
+        _get_prediction = partial(get_prediction, state_pred_token = state_pred_token, action_tokens = action_tokens, reward_tokens = reward_tokens, agent_tokens = agent_tokens)
 
         # forward the network