fix generation so that one more step is taken to decode agent embeds off the final cleaned set of latents, update readme

README.md

@@ -1,9 +1,75 @@
 <img src="./dreamer4-fig2.png" width="400px"></img>
 
-## Dreamer 4 (wip)
+## Dreamer 4
 
 Implementation of Danijar's [latest iteration](https://arxiv.org/abs/2509.24527v1) for his [Dreamer](https://danijar.com/project/dreamer4/) line of work
 
+## Install
+
+```bash
+$ pip install dreamer4
+```
+
+## Usage
+
+```python
+import torch
+from dreamer4 import VideoTokenizer, DynamicsWorldModel
+
+# video tokenizer, learned through MAE + lpips
+
+tokenizer = VideoTokenizer(
+    dim = 512,
+    dim_latent = 32,
+    patch_size = 32,
+    image_height = 256,
+    image_width = 256
+)
+
+# dynamics world model
+
+dynamics = DynamicsWorldModel(
+    dim = 512,
+    dim_latent = 32,
+    video_tokenizer = tokenizer,
+    num_discrete_actions = 4,
+    num_residual_streams = 1
+)
+
+# state, action, rewards
+
+video = torch.randn(2, 3, 10, 256, 256)
+discrete_actions = torch.randint(0, 4, (2, 10, 1))
+rewards = torch.randn(2, 10)
+
+# learn dynamics / behavior cloned model
+
+loss = dynamics(
+    video = video,
+    rewards = rewards,
+    discrete_actions = discrete_actions
+)
+
+loss.backward()
+
+# do the above with much data
+
+# then generate dreams
+
+dreams = dynamics.generate(
+    10,
+    batch_size = 2,
+    return_decoded_video = True,
+    return_for_policy_optimization = True
+)
+
+# learn from the dreams
+
+actor_loss, critic_loss = dynamics.learn_from_experience(dreams)
+
+(actor_loss + critic_loss).backward()
+```
+
 ## Citation
 
 ```bibtex
@@ -17,3 +83,5 @@ Implementation of Danijar's [latest iteration](https://arxiv.org/abs/2509.24527v
     url     = {https://arxiv.org/abs/2509.24527}, 
 }
 ```
+
+*the conquest of nature is to be achieved through number and measure - angels to Descartes in a dream*

dreamer4/dreamer4.py

@@ -2429,6 +2429,7 @@ class DynamicsWorldModel(Module):
         normalize_advantages = None,
         eps = 1e-6
     ):
+        assert isinstance(experience, Experience)
 
         latents = experience.latents
         actions = experience.actions
@@ -2441,7 +2442,7 @@ class DynamicsWorldModel(Module):
         step_size = experience.step_size
         agent_index = experience.agent_index
 
-        assert all([*map(exists, (old_log_probs, actions, old_values, rewards, step_size))]), 'the generations need to contain the log probs, values, and rewards for policy optimization'
+        assert all([*map(exists, (old_log_probs, actions, old_values, rewards, step_size))]), 'the generations need to contain the log probs, values, and rewards for policy optimization - world_model.generate(..., return_log_probs_and_values = True)'
 
         batch, time = latents.shape[0], latents.shape[1]
 
@@ -2694,12 +2695,22 @@ class DynamicsWorldModel(Module):
         return_rewards_per_frame = False,
         return_agent_actions = False,
         return_log_probs_and_values = False,
+        return_for_policy_optimization = False,
         return_time_kv_cache = False,
         store_agent_embed = True,
         store_old_action_unembeds = True
 
     ): # (b t n d) | (b c t h w)
 
+        # handy flag for returning generations for rl
+
+        if return_for_policy_optimization:
+            return_agent_actions |= True
+            return_log_probs_and_values |= True
+            return_rewards_per_frame |= True
+
+        # more variables
+
         has_proprio = self.has_proprio
         was_training = self.training
         self.eval()
@@ -2769,6 +2780,19 @@ class DynamicsWorldModel(Module):
 
             curr_time_steps = latents.shape[1]
 
+            # determine whether to take an extra step if
+            # (1) using time kv cache
+            # (2) decoding anything off agent embedding (rewards, actions, etc)
+
+            take_extra_step = (
+                use_time_kv_cache or
+                return_rewards_per_frame or
+                store_agent_embed or
+                return_agent_actions
+            )
+
+            # prepare noised latent / proprio inputs
+
             noised_latent = randn((batch_size, 1, self.num_video_views, *latent_shape), device = self.device)
 
             noised_proprio = None
@@ -2776,7 +2800,10 @@ class DynamicsWorldModel(Module):
             if has_proprio:
                 noised_proprio = randn((batch_size, 1, self.dim_proprio), device = self.device)
 
-            for step in range(num_steps):
+            # denoising steps
+
+            for step in range(num_steps + int(take_extra_step)):
+
                 is_last_step = (step + 1) == num_steps
 
                 signal_levels = full((batch_size, 1), step * step_size, dtype = torch.long, device = self.device)
@@ -2819,6 +2846,11 @@ class DynamicsWorldModel(Module):
                 if use_time_kv_cache and is_last_step:
                     time_kv_cache = next_time_kv_cache
 
+                # early break if taking an extra step for agent embedding off cleaned latents for decoding
+
+                if take_extra_step and is_last_step:
+                    break
+
                 # maybe proprio
 
                 if has_proprio:
@@ -3021,7 +3053,7 @@ class DynamicsWorldModel(Module):
         latent_is_noised = False,
         return_all_losses = False,
         return_intermediates = False,
-        add_autoregressive_action_loss = False,
+        add_autoregressive_action_loss = True,
         update_loss_ema = None,
         latent_has_view_dim = False
     ):

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "dreamer4"
-version = "0.0.102"
+version = "0.1.0"
 description = "Dreamer 4"
 authors = [
     { name = "Phil Wang", email = "lucidrains@gmail.com" }